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Title	Description	Link
A Comparative Study of State-of-the-Art Machine Learning Algorithms for Predictive Maintenance	The results show that, when the data is scarce, the temporal convolutional network performs better than the common deep learning approaches applied to predictive maintenance, but it does not beat the more traditional feature engineering based approaches.	https://ieeexplore.ieee.org/document/9003044
A Data-Driven Maintenance Framework Under Imperfect Inspections for Deteriorating Systems Using Multitask Learning-Based Status Prognostics	In this article, a data-driven, condition-based maintenance framework (DCBM) for deteriorating equipment under the impact of varying environments and natural aging is proposed, where the equipment degradation status is determined by a prognostic and health monitoring method.	https://ieeexplore.ieee.org/document/9310204
A deep attention based approach for predictive maintenance applications in IoT scenarios	In this article , a multi-head attention (MHA) mechanism was proposed to obtain both high RUL estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.	https://www.emerald.com/insight/content/doi/10.1108/JMTM-02-2022-0093/full/html
A Deep Gaussian Process Approach for Predictive Maintenance	In this article , a deep Gaussian process approach is proposed to predict the expected RUL and estimate the associated variance, which adopts the multilayer architecture such that the predicted result is robust against the selection of kernel functions.	https://ieeexplore.ieee.org/document/9875057
A deep learning approach for integrated production planning and predictive maintenance	In this paper , an integrated predictive maintenance and production planning framework using deep learning and mathematical programming is proposed to minimize the sum of maintenance, setup, holding, backorder, and production costs.	https://www.tandfonline.com/doi/full/10.1080/00207543.2022.2162618
A Deep Learning Approach for Short Term Prediction of Industrial Plant Working Status	In this article, a framework for predictive maintenance is presented, which is built upon a deep learning model based on Long-Short Term Memory Neural Networks, LSTM and CNN.	https://ieeexplore.ieee.org/document/9564391
A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders	In this article, the authors investigated an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones, allowing for maintenance activities to be planned according to actual operational status of the machine and not in advance.	https://www.mdpi.com/1424-8220/21/3/972
A deep learning predictive model for selective maintenance optimization	In this paper , a predictive selective maintenance framework using deep learning and mathematical programming is developed to identify a subset of maintenance actions to perform on the components, and the objective is to minimize the total cost under intermission break time limitation.	https://www.sciencedirect.com/science/article/abs/pii/S095183202100675X?via%3Dihub
A deep learning-based approach for electrical equipment remaining useful life prediction	In this paper , a data-driven approach is proposed to predict the remaining useful life (RUL) of the low-voltage contactor, where failure modes are mainly fusion welding and explosion and a few are unable to switch on.	https://link.springer.com/article/10.1007/s43684-022-00034-2
A Hybrid Deep Learning Framework for Intelligent Predictive Maintenance of Cyber-physical Systems	A practical and effective hybrid deep learning multi-task framework integrating the advantages of convolutional neural network and long short-term memory (LSTM) neural network to reflect the relatedness of remaining useful life prediction with health status detection process for complex multi-object systems in CPS environment is developed.	https://dl.acm.org/doi/10.1145/3486252
A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance	This paper presents a hybrid Convolutional Neural Network Based and Long Short-Term Memory Network (CNN-LSTM) approach to a predictive maintenance problem and demonstrates better prediction accuracy compared to the regular LSTM.	https://ijece.iaescore.com/index.php/IJECE/article/view/24978
A Methodology for Maintenance Analysis and Modeling Using Deep Learning	In this article , the authors presented a methodology for applying topic modeling and deep learning to unstructured maintenance data, for improving availability and cost savings, using a 3-parameter Weibull model to determine the reliability profile.	https://ieeexplore.ieee.org/document/10088232
A novel approach for predictive maintenance combining GAF encoding strategies and deep networks	In this article, the authors exploit GAF (Gramian Angular Field) encoding to obtain images from time series related to production systems, which can be used in pre-trained convolutional neural networks for better prediction performance and thus to create a more efficient and simple predictive maintenance scheme.	https://ieeexplore.ieee.org/document/9356422
A Novel Predictive Maintenance Method Based on Deep Adversarial Learning in the Intelligent Manufacturing System	Wang et al. as discussed by the authors proposed a predictive maintenance (PDM) method based on the improved deep adversarial learning (LSTM-GAN) which can solve the disadvantage of vanishing gradients and the mode collapse from the GAN.	https://ieeexplore.ieee.org/document/9388687/
A Novel Predictive Selective Maintenance Strategy Using Deep Learning and Mathematical Programming	In this paper , a predictive selective maintenance strategy is proposed to solve complex and relatively large multi-component systems, where a DL algorithm is used to estimate the probability that each component will successfully complete the upcoming mission, and a selective maintenance optimization model is then used to identify the maintenance actions that will maximize the system reliability.	https://www.sciencedirect.com/science/article/pii/S2405896322018626?via%3Dihub
A Predictive Maintenance Strategy Using Deep Learning Quantile Regression and Kernel Density Estimation for Failure Prediction	Wang et al. as discussed by the authors proposed an ensemble model of deep autoencoder (DAE), long short-term memory (LSTM), quantile regression (QR), and kernel density estimation (KDE) to predict system failure.	https://ieeexplore.ieee.org/document/10026825
A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach	In this paper , a deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks is proposed to handle extremely rare failure predictions in aircraft predictive maintenance modelling.	https://link.springer.com/article/10.1007/s00521-022-07167-8
Adopting a Deep Learning Split-Protocol Based Predictive Maintenance Management System for Industrial Manufacturing Operations	In this paper , a failure detection system that focuses only most probable failure state at maximum utilization and is delicate in incoming jobs to the backup unit while the overloaded unit will recover and resume in a very fresh state.	https://link.springer.com/chapter/10.1007/978-981-99-2233-8_2
Application of Deep Learning for Predictive Maintenance of Oilfield Equipment	In this paper , the authors explored applications of the new emerging techniques of artificial intelligence and deep learning (neural networks in particular) for predictive maintenance, diagnostics and prognostics.	https://www.researchgate.net/publication/370594918_Application_of_Deep_Learning_for_Predictive_Maintenance_of_Oilfield_Equipment?channel=doi&linkId=6458880e809a5350215818a6&showFulltext=true
Artificial intelligence for predictive maintenance	The application of artificial intelligence is presented to create a model that can successfully predict the condition of a machine in terms of the probability of failure occurrence and an artificial neural network was trained and it successfully predicted the state of the machine.	https://iopscience.iop.org/article/10.1088/1742-6596/2299/1/012001
Automating predictive maintenance using oil analysis and machine learning	This research investigates the use of random forests, feed-forward neural networks and logistic regression models trained using oil analysis data for classifying machine conditions and finds the RF model outperformed the other classifiers for all machine conditions.	https://ieeexplore.ieee.org/document/9041003
Comparison of Different Machine Learning Algorithms for Predictive Maintenance	In this article , a data-driven modeling approach is described for the investigation of tool wear and bearing failures, based on machine failure and the quality of tools, and a predictive maintenance strategy is proposed to reduce the cost of downtime and raise the availability of industrial equipment.	https://ieeexplore.ieee.org/document/10080334
Computer Big Data Analysis and Predictive Maintenance Based on Deep Learning	This paper introduced the computer big data analysis and predictive maintenance method based on deep learning, and the performance of different Convolutional Neural Network models was compared and the results proved the effectiveness of the proposed model.	https://www.iieta.org/journals/isi/paper/10.18280/isi.270220
Conditional Predictive Maintenance of Electric Vehicles from Electrical and Mechanical Faults	In this paper , the authors used the available sensor data of the electric vehicle from various electronic control units and designed a predictive model which classifies the various electrical and mechanical faults that occur in an electric vehicle and predicts the types for increasing the reliability of the whole electrical vehicular system.	https://www.ijfmr.com/research-paper.php?id=1325
Continual Learning for Predictive Maintenance: Overview and Challenges	In this article , the authors present a brief introduction to predictive maintenance, nonstationary environments, and continual learning, together with an extensive review of the current state of applying continual learning in real-world applications and specifically in predictive maintenance.	https://www.sciencedirect.com/science/article/pii/S2667305323000765?via%3Dihub
Data-Driven Predictive Maintenance in Evolving Environments: A Comparison Between Machine Learning and Deep Learning for Novelty Detection	In this paper, a comparison between ML and deep learning methods for novelty detection is conducted, to evaluate their effectiveness and efficiency in different scenarios, and the results show the superiority of DL on traditional ML methods in all the evaluated scenarios.	https://link.springer.com/chapter/10.1007/978-981-16-6128-0_11
Deep Convolutional Autoencoder Architecture for Predictive Maintenance Applications	In this article , an auto-encoder extension of previously proposed deep convolutional network was trained successfully on the modelling of electroencephalogram (EEG) signals with high performance.	https://ieeexplore.ieee.org/document/9864836
Deep digital maintenance	The planning module of DDM is investigated in more detail with realistic industrial data from earlier case studies and shows that both the remaining useful life (RUL) and the expected profit loss indicator (PLI) of ignoring the failure can be calculated for the planning module.	https://link.springer.com/article/10.1007/s40436-017-0202-9
Deep Learning for Data-Driven Predictive Maintenance	Deep learning algorithms have shown profound progress in the problem areas where practitioners and researchers had been eluded for several decades as discussed by the authors, which can be used to identify equipment failures in parts or as a whole.	https://link.springer.com/chapter/10.1007/978-3-030-75490-7_3
Deep Learning in the Maintenance Industry	The work hereby presented was born from a desire to find how modern machine learning methods can be applied to improve scheduling in the maintenance industry, and proposes two methods: forecasting workload and using AI to improve human lives instead of replacing human workers.	https://arrow.tudublin.ie/ittthedoc/8/
Deep learning models for predictive maintenance: a survey	This Springer article provides a survey of deep learning models for predictive maintenance.	https://link.springer.com/article/10.1007/s10489-021-03004-y
Deep Learning–Based Predictive Maintenance of Photovoltaic Panels	In this paper , the authors present a deep learning-based arrangement of a predictive maintenance system for solar PV panels to guarantee ideal yield from the individual PV panels over the long run.	https://www.taylorfrancis.com/chapters/edit/10.1201/9781003202288-7/deep-learning%E2%80%93based-predictive-maintenance-photovoltaic-panels-yuvaraj
Deep-Learning-Enabled Predictive Maintenance in Industrial Internet of Things: Methods, Applications, and Challenges	In this paper , the authors provide a comprehensive survey of DL-based intelligent predictive maintenance (IPdM) for the researchers and practitioners who focused on the promotion of fault diagnosis/prognosis.	https://ieeexplore.ieee.org/document/9851995
Deep-Reinforcement-Learning-Based Predictive Maintenance Model for Effective Resource Management in Industrial IoT	In this paper , a model-free deep reinforcement learning (DRL)-based predictive maintenance (PdM) framework is proposed to automatically learn an optimal decision policy from a stochastic environment.	https://ieeexplore.ieee.org/document/9528837
Defining and Implementing Predictive Maintenance Based on Artificial Intelligence for Rotating Machines	In this paper , the authors proposed a new model that summarizes a Deep Learning based approach to real-time bearing diagnosis and prognosis, which supports failure reduction and preventive inspection and replacement actions for the implementation of predictive maintenance.	https://link.springer.com/chapter/10.1007/978-3-031-29860-8_83
Digital twin predictive maintenance strategy based on machine learning improving facility management in built environment	In this article , a predictive maintenance strategy to reduce mechanical and electrical plants malfunctioning for residential technical plant systems is presented, which can guarantee a tailored maintenance service based on machine learning systems, drastically reducing breakdowns after a maximum period of 3 years.	https://www.sciencedirect.com/science/article/abs/pii/B9780128207932000070?via%3Dihub
DOWELL: Diversity-Induced Optimally Weighted Ensemble Learner for Predictive Maintenance of Industrial Internet of Things Devices	In this paper , the authors proposed an ensemble learning framework, where accurate and diverse base learners are selected out of 20 different state-of-the-art deep learning models, and the optimal weights of base learners were discovered by constructing an optimization problem.	https://ieeexplore.ieee.org/document/9484087/
EIT Deep Tech Talent Initiative	This is a pioneering programme led by the European Institute of Technology and Innovation (EIT) that aims to skill one million people within deep tech fields over the next three years. The initiative offers deep tech courses and funds the development and scaling of new or existing learning materials in deep tech.	https://www.eitdeeptechtalent.eu/the-initiative/
Machine Learning and IIoT Application for Predictive Maintenance	In this paper , the authors discuss the use of ML with Industrial Internet of Things (IIoT) from a data analysis perspective and compare different traditional ML and deep learning techniques with their potential use in predictive maintenance.	https://link.springer.com/chapter/10.1007/978-981-19-9338-1_32
Modeling Deep Neural Networks to Learn Maintenance and Repair Costs of Educational Facilities	This study attempted to propose an analytical modeling framework that can accurately learn various factors, significantly affecting the maintenance and repair costs of educational facilities, and can contribute to the existing body of knowledge.	https://www.mdpi.com/2075-5309/11/4/165
Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines	In this paper , a multi-agent approach that learns a maintenance policy performed by technicians, under the uncertainty of multiple machine failures, is proposed to coordinate the decision-making in maintenance scheduling, resulting in the dynamic assignment of maintenance tasks to technicians.	https://www.sciencedirect.com/science/article/pii/S0736584522000928?via%3Dihub
Noise-robust Machine Learning Models for Predictive Maintenance Applications	In this paper , noise-robust predictive maintenance models, which include ensemble and deep learning models with and without data fusion, are proposed to enhance the monitoring of industrial equipment, with sound, vibration, and ultrasound data collected in real experiments.	https://ieeexplore.ieee.org/document/10122864/
Performance Assessment of Customized LSTM based Deep Learning Model for Predictive Maintenance of Transformer	In this article , a customized LSTM network named C-LSTM is devised to circumvent the boundaries of the standard LSTMs, which had an increased rate of classification error than conventional machine learning techniques.	https://ijeer.forexjournal.co.in/archive/volume-11/ijeer-110220.html
Performance comparison of machine learning algorithms for predictive maintenance	The conducted research allowed to conclude that in the analysed case, the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records, which represents the future of machine reliability in industry.	https://ph.pollub.pl/index.php/iapgos/article/view/1834
Prediction method of equipment maintenance time based on deep learning	A RCNN deep learning model is proposed for training and feature extraction of equipment maintenance service information and geographical environment information, which can also predict the equipment maintenance interval.	https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11565/2575725/Prediction-method-of-equipment-maintenance-time-based-on-deep-learning/10.1117/12.2575725.short
Predictive Maintenance - Exploring strategies for Remaining Useful Life (RUL) prediction	In this article , the authors presented a generic strategy for exploring, analyzing and predicting the value of RUL and identifying techniques for specific data modeling, using a deep learning model, with a LSTM (Long Short-Term Memory) architecture.	https://ieeexplore.ieee.org/document/10053988
Predictive Maintenance for Commercial Vehicles Tyres Using Machine Learning	In this paper , a machine learning-based regression model is presented to predict the remaining useful life of a commercial vehicle tyre based on vehicle and tyre past and current condition, and performance.	https://ieeexplore.ieee.org/document/9984497
Predictive Maintenance for data engineers - EIT Campus	This course on EIT Campus teaches how to solve Predictive Maintenance tasks guided by Artificial Intelligence principles and how to compare Equipment Availability attributes for informed decision-making.	https://eit-campus.eu/course/manufacturing/predictive-maintenance-for-data-engineers
Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach	A model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context that self-learns an optimal maintenance policy and provides actionable recommendation for each equipment.	https://ieeexplore.ieee.org/document/9221098
Predictive Maintenance for Increasing EV Charging Load in Distribution Power System	In this paper, the authors proposed a deep reinforcement learning (RL) based policy to replace the distribution transformers by similar or higher capacity ones under a budgetary constraint of selecting at most one transformer for replacement per time step.	https://ieeexplore.ieee.org/document/9303021
Predictive Maintenance for Maintenance-Effective Manufacturing Using Machine Learning Approaches	In this paper , an innovative methodology for model training is proposed that aims to improve model performance while also allowing for continuous training, and an automatic hyperparameter tunning approach for the gradient boosting and support vector machine (SVM) models is proposed.	https://link.springer.com/chapter/10.1007/978-3-031-18050-7_2
Predictive maintenance for offshore oil wells by means of deep learning features extraction	In this article , a deep learning approach for feature extraction in the offshore oil well monitoring context, exploiting the public 3 W dataset, has been proposed, which is made up of about 2000 multivariate time series labelled according to the corresponding functioning of the well.	https://onlinelibrary.wiley.com/doi/10.1111/exsy.13128
Predictive Maintenance for Remote Field IoT Devices—A Deep Learning and Cloud-Based Approach	This SpringerLink article discusses predictive maintenance for remote field IoT devices using a deep learning and cloud-based approach.	https://link.springer.com/chapter/10.1007/978-981-99-0835-6_40
Predictive Maintenance Model for IIoT-Based Manufacturing: A Transferable Deep Reinforcement Learning Approach	Li et al. as mentioned in this paper proposed a generic PdM optimization framework to assist maintenance teams in prioritizing and resolving maintenance task conflicts under real-world manufacturing conditions, which jointly optimized the edge-based machine network uptime and the allocation of manpower resources in a stochastic IIoT-enabled manufacturing environment using the model-free deep reinforcement learning (DRL) methods.	https://ieeexplore.ieee.org/document/9714509
Predictive Maintenance of Aircraft Engine using Deep Learning Technique	In this article, an accurate algorithm to estimate the remaining useful life of an aircraft engine is proposed, which utilizes the combination of CNN and LSTM algorithms in learning the behavior of the historical data and providing the accurate information about the time to failure of the system.	https://ieeexplore.ieee.org/document/9289466
Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review	In this article , a comprehensive review of recent works of DL techniques that are applied to PM for electromechanical systems by classifying the research according to equipment, fault, parameters, and method is presented.	https://www.iieta.org/journals/isi/paper/10.18280/isi.270618
Predictive Maintenance of Industrial Equipment using Deep Learning: from sensory data to remaining useful life estimation	In this article , the authors apply a deep learning model approach to estimate equipment remaining useful life from typical raw sensory data of equipment monitored over a time period, and compare the performance with a recurrent neural network architecture and a multilayer perceptron.	https://ieeexplore.ieee.org/document/9967582
Predictive Maintenance of Norwegian Road Network Using Deep Learning Models	In this paper , the authors explored the use of deep neural networks to classify roads based on the amount of deterioration by training the network to identify various types of cracks, corrugation, upheaval, potholes, and other types of road damage.	https://www.mdpi.com/1424-8220/23/6/2935
Predictive maintenance of photovoltaic panels via deep learning	It is demonstrated, by means of numerical experiments, that the proposed convolutional neural networks method is able to predict accurately the power curve of a functioning panel and outperforms the existing approaches that are based on simple interpolation filters.	https://ieeexplore.ieee.org/document/8439898/
Predictive Maintenance of Thermal-Energy-Storage Air-Conditioning with Deep Learning	In this paper , a thermal energy storage air conditioner (TES-AC) is used to store chilled water in the form of thermal energy at night when energy demand is low and then uses the stored thermal energy to cool the building's air the next day.	https://www.scienceopen.com/hosted-document?doi=10.14293/S2199-1006.1.SOR-.PPERLMU.v1
Predictive Maintenance using Machine Learning	In this paper , a predictive maintenance (PdM) is implemented to effectively manage maintenance plans of the assets by predicting their failures with data-driven techniques, where data is collected over a certain period of time to monitor the state of equipment.	https://arxiv.org/abs/2205.09402
Predictive Maintenance using Machine Learning Based Classification Models	In this paper, several predictive models using machine learning on the Semiconductor Manufacturing process dataset (SECOM) were applied in order to facilitate predictive maintenance due to the advantages it holds over traditional methods of maintaining semi-conductor devices such as preventive and breakdown maintenance.	https://iopscience.iop.org/article/10.1088/1757-899X/954/1/012001
Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot	In this paper, an autoencoder is used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data are available.	https://www.mdpi.com/1424-8220/21/21/6979
Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing	Wang et al. as mentioned in this paper proposed an intelligent predictive maintenance approach for machine tools via multiple services cooperating within a single framework, which is supported by the combination of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM).	https://www.sciencedirect.com/science/article/abs/pii/S073658452200045X?via%3Dihub
Scaling Up Deep Learning Based Predictive Maintenance for Commercial Machine Fleets: a Case Study	This paper summarizes results of an extensive project, developing a deep learning based fault detection scheme for wind farms, and emphasizes the elements of this scheme that enabled scaling it up for commercial implementation which took place recently.	https://ieeexplore.ieee.org/document/9911976
Simultaneous Prediction of Remaining-Useful-Life and Failure-Likelihood with GRU-based Deep Networks for Predictive Maintenance Analysis	In this paper, the authors proposed a solution to predict RUL and failure detection simultaneously within the same network based on GRUs, and compared the performance of GRU layers to LSTM and RNN layers and reported their performance on NASA dataset.	https://ieeexplore.ieee.org/document/9522592
Smart predictive maintenance for high-performance computing systems: a literature review	A significant upward trend is shown in the use of deep learning methods of sensor data collected by mission critical assets for early failure detection to assist predictive maintenance schedules.	https://link.springer.com/article/10.1007/s11227-021-03811-7
Survey on Deep Learning applied to predictive maintenance	This essay outlines the challenges at each level with the type of improvement that has been made in deep learning over the last 3 years, and offers an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model.	https://ijece.iaescore.com/index.php/IJECE/article/view/21550
Using deep learning to value free-form text data for predictive maintenance	Past maintenance logs may encapsulate meaningful data for predicting the duration of machine breakdowns, the potential causes of a problem, or the necessity to stop production to perform repair act.	https://www.tandfonline.com/doi/full/10.1080/00207543.2021.1951868
Using Long-Short term Memory networks with Genetic Algorithm to predict engine condition	In this article , a new deep neural network (DNN) architecture is proposed to bring a different approach to the predictive maintenance domain, which consists of an input layer, a Long-Short Term Memory (LSTM), a dropout layer (DO) followed by an LSTM layer, hidden layer, and an output layer.	https://dergipark.org.tr/en/pub/gujs/issue/69473/937169
Industry 4.0-potentials for predictive maintenance	This paper investigates the potentials and trends of predictive maintenance and maintenance management in industrial big data and Cyber-Physical Systems environment. Furthermore, the development of predictive maintenance, its technical challenges, and the potentials under Industry 4.0 era was researched to discover the linkage between Industry 4.0 and predictive maintenance.	https://www.atlantis-press.com/proceedings/iwama-16/25862217
A Survey of Predictive Maintenance: Systems, Purposes and Approaches	This paper highlights the importance of maintenance techniques in the coming industrial revolution, reviews the evolution of maintenance techniques, and presents a comprehensive literature review on the latest advancement of maintenance techniques, i.e., Predictive Maintenance (PdM), with emphasis on system architectures, optimization objectives, and optimization methods.	https://arxiv.org/abs/1912.07383
Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data	In this paper, the AI-based algorithms for predictive maintenance are presented, and are applied to monitor two critical machine tool system elements: the cutting tool and the spindle motor. A data-driven modeling approach will be described, and it will be utilized to investigate the tool wear and the bearing failures.	https://www.sciencedirect.com/science/article/pii/S2212827118312988
Towards Using Digital Intelligent Assistants to Put Humans in the Loop of Predictive Maintenance Systems	Digital Intelligent Assistants (DIAs) provide fast, intuitive, and potentially hands-free access to systems through voice-based interaction and cognitive assistance. This paper introduces a novel approach to interact with predictive maintenance systems through DIAs. The aim is to integrate human knowledge more effectively into the predictive maintenance process to create a hybrid-intelligence system. In such systems, humans and computers complement and evolve together.	https://www.sciencedirect.com/science/article/pii/S2405896321007047
On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges	This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle of maintenance projects and the challenges encountered, and presents the models associated with this type of maintenance: condition-based maintenance (CBM), prognostics and health management (PHM), and remaining useful life (RUL). Finally, a novel applied industrial workflow of predictive maintenance is presented including the decision support phase wherein a recommendation for a predictive maintenance platform is presented. This platform ensures the management and fluid data communication between equipment throughout their life cycle in the context of smart maintenance.	https://www.mdpi.com/2076-3417/12/16/8081
Human-machine interface in smart factory: A systematic literature review	A smart factory is a form of manufacturing in the era of Industry 4.0 that has adopted new integrated manufacturing technologies. The importance of human–machine interfaces (HMIs) has been increasing due to the complexity of the current manufacturing context. Therefore, it is necessary to identify and understand HMIs in smart factories from a holistic perspective, which enables us to understand the overall picture. In this study, we conducted a systematic literature review (SLR) of HMIs to identify smart factory functions, tasks, information types, interaction modalities, and their impact on human operators from the perspectives of human factors and human–computer interaction.	https://www.sciencedirect.com/science/article/abs/pii/S0040162521007186
Substantial capabilities of robotics in enhancing industry 4.0 implementation	The paper discusses eighteen major applications of Robotics for Industry 4.0. Robots are ideal for collecting mysterious manufacturing data as they operate closer to the component than most other factory machines. This technology is helpful to perform a complex hazardous job, automation, sustain high temperature, working entire time and for a long duration in assembly lines.	https://www.sciencedirect.com/science/article/pii/S2667241321000057
From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry	This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things.	https://www.mdpi.com/1424-8220/23/13/5970
Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0	This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.	https://www.mdpi.com/2071-1050/12/19/8211
The quality management ecosystem for predictive maintenance in the Industry 4.0 era	This study presents new ideas for predictive quality management based on an extensive review of the literature on quality management and five real-world cases of predictive quality management based on new technologies. The results of the study indicate that advanced technology enabled predictive maintenance can be applied in various industries by leveraging big data analytics, smart sensors, artificial intelligence (AI), and platform construction.	https://link.springer.com/article/10.1186/s40887-019-0029-5
Predictive Maintenance in Industry 4.0 for the SMEs: A Decision Support System Case Study Using Open-Source Software	This study presents a prototype decision support system (DSS) that collects and processes data from many sensors and uses machine learning and artificial intelligence algorithms to report deviations from the optimal process in a timely manner and correct them to the correct parameters directly or indirectly through operator intervention or self-correction.	https://www.mdpi.com/2411-9660/7/4/98

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Videos

Title	Description	Link
3 benefits of computer preventive maintenance	Computer preventive maintenance is very important to keep your machines running smoothly can securely. Click the link above to contact one of Syn-Star's specialists today to learn how we can help you with computer preventive maintenance.	https://www.youtube.com/watch?v=vqFTr4Pklgs
Beyond Predictive & Preventive Maintenance	Did you know you could save up to 18% with a Predictive Maintenance Management Strategy? Predictive and Preventive Maintenance is a well-known term and most know what it entails but have you looked past the strategy checklist? Join this webinar for a more in-depth look beyond Predictive and Preventive Maintenance.	https://www.youtube.com/watch?v=huHSJGU4WFQ
Common mistakes with preventive maintenance programs and how to overcome them.	A well run engineering department is vital to running an efficient and safe operation. But how do you know if your maintenance program is effective and what can you do to take it to the next level?	https://www.youtube.com/watch?app=desktop&v=OjCx_sQnvHc
Condition-Based Maintenance vs. Predictive Maintenance	Learn about condition-based maintenance (CBM), which is a maintenance strategy based on the current health state of the equipment. The current health condition is computed by an algorithm using sensor data collected from the equipment. According to condition-based maintenance, a maintenance action would be planned only after faulty behavior has been detected.	https://ch.mathworks.com/videos/condition-based-maintenance-vs-predictive-maintenance-1657262361263.html
Electric Motor Maintenance- Preventive Maintenance for Motors on Farms	If you have a farm, you're bound to have plenty of electric motors. Electric motor maintenance is essential to the longevity of a successful farm and a reliable operation. Performing maintenance regularly can save you loads of time and money! You likely do not have the time to hire qualified personnel to regularly check your electric motors, so we are hoping to help you out with a little information to use yourself.	https://www.youtube.com/watch?v=uqEMd9jviWI
Energy Speaker Series - Module 2: Utility Asset Condition Monitoring and Predictive Maintenance using Machine Learning and Artificial Intelligence	In a wind turbine, a failure of an important component, such as a main bearing, can lead to long-lasting downtimes and thus to a corresponding energy loss. In offshore wind energy, the problem is even more serious as maintenance work is not always possible due to adverse weather conditions and must be planned in advance. In order to save operational expenditure, wind farm operators are required to implement a maintenance strategy that enables them to predict a component’s failure as early as possible.	https://ch.mathworks.com/videos/utility-asset-condition-monitoring-and-predictive-maintenance-using-machine-learning-and-artificial-intelligence-1606330172119.html
Episode 1: Introduction to Preventive Maintenance	In this video series we are going to identify the differences between preventing equipment breakdown and addressing breakdown when it occurs within an operational program.	https://www.youtube.com/watch?v=_5VqhGRwjg4
GraceSense™\| Predictive Maintenance System: Case Study	The field-proven Predictive Maintenance System from GraceSense™ has already saved one company millions in reduced unplanned downtime incidents and quicker response times, making reactive maintenance a thing of the past.	https://www.youtube.com/watch?v=bxy8-uMvA_w
Grove GMK preventive maintenance (Manitowoc Crane Care)	To protect your investment, experience shows that spending a small amount of time and effort on preventive maintenance will help keep your GMK in good conditions for many years.	https://www.youtube.com/watch?app=desktop&v=AMxvGfy_X6I&embeds_referring_euri=https%3A%2F%2Fspb.gde.ru%2F&feature=emb_imp_woyt
How to Schedule Weekly Equipment Preventive Maintenance \| Microsoft Access Database \| Email Reminder	This Microsoft access equipment maintenance databases is created for a production/manufacturing environment to conduct preventive and reactive maintenance, to write work orders, maintain inventories, and monitor continuous improvement project. Microsoft access database is needed to use this product. unlike most database in the market, this access database requires no plug-in update, no version update, simple one and done. Preventive maintenance – schedule Daily equipment maintenance, weekly equipment maintenance, fortnightly equipment maintenance, monthly equipment maintenance, quarterly equipment maintenance, Biyearly equipment maintenance, Yearly equipment maintenance.	https://www.youtube.com/watch?v=iBFEnYNwuIc
How to... Network Redundancy and Preventive Maintenance Data	In this video we will cover how to implement Network Redundancy (Ring Topology) and Preventive maintenance data in Profinet system.	https://www.youtube.com/watch?v=Y5xtfqZieqk
Industry 5.0 Unveiled: Embracing Tomorrow's Technological Revolution	Dive deep into the latest innovations and transformative technologies shaping the next industrial revolution. Discover how interconnected systems, AI, and advanced manufacturing are revolutionizing industries. Join us at the forefront of progress as we decode the mysteries of Industry 5.0 – where efficiency meets innovation!	https://www.youtube.com/watch?v=sGI2FFm_8tw
Intelligent Manufacturing with Edge MCM Quick Start	SAS, together with partners ADLINK and Intel, brings the power of IoT edge-to-cloud analytics to manufacturing environments. With Edge MCM Quick Start, machinery operations managers can better monitor and manage complex machinery.	https://www.youtube.com/watch?v=fgDQ6pbElEQ
Introduction \| Predictive Maintenance, Part 1	This video explains different maintenance strategies and walks you through a workflow for developing a predictive maintenance algorithm. Predictive maintenance lets you find the optimum time to schedule maintenance by estimating time to failure. It also pinpoints problems in your machinery and helps you identify the parts that need to be fixed. Using predictive maintenance, you can minimize downtime and maximize equipment lifetime.	https://ch.mathworks.com/videos/predictive-maintenance-part-1-introduction-1545827554336.html
Kyklos 4.0 - Predictive Maintenance via Deep Learning	KYKLOS 4.0 will demonstrate, in a realistic, measurable, and replicable way the transformative effects that CPS, PLM (Product Life Management), LCA (Life Cycle Analysis), AR (Augmented Reality) and AI technologies and methodologies will have on the Circular Manufacturing Framework	https://www.youtube.com/watch?v=66C6dMK9Wk0
Maintenance Philosophies - Breakdown Maintenance, Preventive Maintenance, Predictive Maintenance	Reliability, Process, and Maintenance (RPM) Symposium. This presentation discusses three different maintenance philosophies: breakdown maintenance, preventive maintenance, and predictive maintenance.	https://www.youtube.com/watch?v=1UCTv7d5V60
Maintmaster CMMS — A powerful CMMS for maintenance pros	Systemise your maintenance success, together Imagine your days running like clockwork, with everyone collaborating and learning from each other with easy ways to measure your results. This maintenance platform gives technicians, mechanics, and managers from all departments a simpler and faster way to do maintenance in line with organisational goals.	https://www.youtube.com/watch?v=cMTlsq9zVik
More performance for power plants: Prevention instead of maintenance	Greater system availability thanks to optimized maintenance procedures. Our Predictive Maintenance solution turns unscheduled maintenance interruptions into predictable intervals. The video shows you how to identify potential defects early on and replace the affected components before the system fails. Find out how predictive maintenance helps you save on maintenance costs.	https://www.youtube.com/watch?v=bdSk7Mj0IuQ
PC Preventive Maintenance	This video chronicles the periodic PC Preventive Maintenance I try to do on all of my heavily used personal computers and servers	https://www.youtube.com/watch?v=RKNuOCaPktM
PdM - How Does Predictive Maintenance Work?	This 2022 Forbes article claims that maintenance activity during planned downtime is 4x more efficient than the same activity during unplanned downtime. That’s a massive difference in maintenance cost if you can predict maintenance events and plan for them. This is why most operators are exploring and implementing predictive maintenance programs anywhere they can. In this video I’ll explain how predictive maintenance works.	https://www.youtube.com/watch?v=A0MQYinji8w
Piston Compressor Preventive Maintenance	Gas piston compressors are used in a variety of applications and provide years of reliable service proper preventative maintenance procedures using genuine gassed replacement parts eliminates unplanned downtime avoids costly repairs and provides trouble-free performance by following the step-by-step instructions in this video.	https://www.youtube.com/watch?v=T2HDVXFskEY
PM100 - Lesson 2.4 - Types of Maintenance	In this lesson, we’ll delve deep into the Principles that underpin an Effective Maintenance Program. This is probably the most important lesson in this whole course. If you can really understand and apply the principles of an effective preventive maintenance program, you are going to succeed on your Road to Reliability™.	https://www.youtube.com/watch?v=N2QpEXEsojA
Predictive Maintenance	Manufacturers of aircraft, engines, propellers and appliances have traditionally called for performing preventive maintenance on a fixed timetable. A prime example is engine and propeller TBOs. More recently, this time-based approach has given way to condition-based preventive maintenance based on regular repetitive inspections. Now we're beginning to see this inspection-driven approach giving way to predictive maintenance based on analysis of data from sensors installed on the aircraft and engine. In this webinar, Mike Busch A&P/IA discusses this latest trend and how its starting to trickle down to owner-flown piston GA.	https://www.youtube.com/watch?v=Q39JQccOJLk
Predictive Maintenance Explained	Every device has a point of failure. What does that mean? Well, a new device fresh from the manufacturer is healthy and problem-free. Due to wear and tear on the device as it ages, its health slowly deteriorates and eventually it fails.	https://www.youtube.com/watch?v=2_o1SDy6__U
Predictive Maintenance for Industrial Robotics \| Robotics for Smart Factory, Part 2	A predictive approach to maintenance operations guarantees advantages such as reducing machine downtime, eliminating unnecessary interventions, and adding revenue streams for equipment vendors with aftermarket services. These benefits are achievable, but there are challenges in implementing predictive maintenance in application fields such as industrial robotics. One obstacle is the limited availability of functioning data and the lack of failure data needed for accurate results. In addition, choosing the best models for monitoring and prediction can be time consuming, and integrating devised algorithms with existing tools and infrastructures can cause a bottleneck in the implementation process. In this talk, we will analyze the benefits and challenges of predictive maintenance, and describe, via the exploration of a real case-study, the solution proposed by MathWorks to overcome these obstacles and realize efficient predictive maintenance systems.	https://ch.mathworks.com/videos/smart-factory-part-2-predictive-maintenance-for-industrial-robotics-1644917979694.html
Predictive Maintenance in Various Industries	Discover how technology reshapes practices across sectors, from manufacturing to healthcare. Learn how it revolutionizes operations, saves costs, and prevents downtime. Ready to embrace the future?Explore the evolution of predictive maintenance in diverse industries, including manufacturing and healthcare. Learn about the benefits, challenges, and implementation strategies.	https://www.youtube.com/watch?v=fzDOhzJBSjg
Predictive maintenance of industrial equipment \| Radix’s AI for Sustainability series	Effective maintenance of industrial equipment can increase energy efficiency and also cut emissions directly by reducing leaks of methane and other greenhouse gases. This presentation will cover how Machine Learning can help.	https://www.youtube.com/watch?v=baOd3VAGlqM
Predictive Maintenance Solution Overview	Equipment breakdowns happen. Best case scenario: you have replacement parts on hand and will be up and running without much delay. Worst case: needed parts aren't in stock, or expert resources aren't available to fix the problem. In either case, it’s best to minimize the occurrence of these situations. Making it “as simple as possible,” that is our approach to managing maintenance predictively with TrakSYS™ as an integral part of digitizing manufacturing operations.	https://www.youtube.com/watch?v=7yHt69qKs0s
Preventative, Predictive & Breakdown Maintenance - What's the Difference?	UpKeep's Asset Operations Management Platform is a mobile-first CMMS, EAM & IIoT suite of solutions proven to streamline the work order process and take you from reactive to preventive maintenance. Purpose-built to manage multi-location, day-to-day maintenance life cycles; optimize asset utilization; and gain insights into real-time performance data. Maintenance, reliability, and operations teams now have one easy-to-use tool to drive efficiency and effectiveness across the organization.	https://www.youtube.com/watch?v=2i8wf_T1CYU
Preventive Maintenance on Computer System \| ICT-CSS RIZAL 10	Preventive Maintenance on Computer System	https://www.youtube.com/watch?v=rFZtVU3EgVE
Revolutionizing Maintenance: Unlocking the Future with Predictive Maintenance and AI	Explore the future of industrial maintenance with our latest video on "Predictive Maintenance with AI." In this insightful presentation, we delve into the transformative power of artificial intelligence in optimizing maintenance strategies, reducing downtime, and maximizing operational efficiency.	https://www.youtube.com/watch?v=c8aFC-Jxuww
SKF helps secure bearings through preventive maintenance	This movie is an example of how SKF's successful preventive and proactive maintenance of bearings can help secure your operations even in the toughest places on earth	https://www.youtube.com/watch?v=5jHoNkV7a8I
Smart Monitoring and Predictive Analytics for Operations (OT) and Manufacturing	With digitization adopted in many industries, real-time data from manufacturing and operational equipment can be used to monitor and optimize operation - by applying data-driven modeling including machine learning. In this video you learn how you can automatically monitor equipment and connected devices, and apply predictive modeling to optimize operations (OT). With this approach, manufacturers and grid operators reduce downtime and save maintenance cost by scheduling equipment maintenance just when needed, connected consumer and wearable medical devices are monitored remotely, and transportation providers optimize operation of their fleets.	https://www.youtube.com/watch?v=twkEYj7HGDE
The 3 Types of Maintenance in Mechanical Engineering	In this informative video, we delve into the fascinating world of maintenance strategies, exploring the three pivotal types: Preventive, Predictive, and Corrective maintenance.	https://www.youtube.com/watch?v=kEzh-YYTr1s
Webinar: Consolidate all your maintenance tools into one enterprise grade CMMS	If you currently have a CMMS for corrective and preventive maintenance, some niche calibrations software and you still struggle during audits, this webinar is for you.	https://www.youtube.com/watch?v=9P6SueDetfE
Webinar: UPS & Battery Maintenance with Fluke	The importance of keeping backup power is essential for so many facilities, including data centres, hospitals, airports, utilities, oil and gas facilities, railways, and the list goes on. Operating without 100% backup power reliability is not an option.	https://www.youtube.com/watch?v=A0DWLWFLiCQ
What Does a Maintenance Engineer Do?	The modern maintenance engineer is an important part of a 's technical maintenance structure. However, job descriptions are becoming more diverse and multifaceted. If you're looking to apply for this position, it pays to know the full set of maintenance engineer responsibilities. In this video, I'll explain in detail what a maintenance engineer does in 2021, what the requirements of the job title are, and the perks that come with it.	https://www.youtube.com/watch?v=JbqiSOSuhC8
What is Predictive Maintenance?	In this video we'll have a look at how maintenance has evolved from checking airplanes every 50 hours to detecting failures before they happen and even predicting the time when a machine stops. Let's talk about predictive maintenance	https://www.youtube.com/watch?v=mveN-1r7XiA
What is Predictive Maintenance? - Decoding Condition-Based Maintenance	In this insightful video, we unravel the mysteries behind predictive maintenance and delve deep into the realm of condition-based maintenance. Discover the key concepts, benefits, and challenges associated with these crucial maintenance strategies.	https://www.youtube.com/watch?v=6hhx-X8azoM
What is Predictive Maintenance? \| Working, Advantages, Disadvantages of Predictive Maintenance	Predictive Maintenance (PdM) is one of the types of maintenance, that monitor the condition and performance of equipment during normal operation. By doing so, maintenance managers and technicians are able to identify possible defects and fix them before they result in failure.	https://www.youtube.com/watch?v=99_o20JsZpc
Why Predictive Maintenance?	In this insightful video, we delve deep into the world of Predictive Maintenance and unravel why it's an absolute game-changer for modern businesses.	https://www.youtube.com/watch?v=gFcvhe8HAw4

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Articoli

Getting Started with Predictive Maintenance: A video explaining different maintenance strategies and steps to develop a predictive maintenance algorithm using MATLAB and Simulink.
Predictive Maintenance & Monitoring using Machine Learning: Demo & Case study: A video showcasing a predictive maintenance solution based on machine learning using Google Cloud Platform.
Free Predictive Maintenance Systems Videos: A collection of free videos about predictive maintenance systems, available on Pexels.
Predictive Maintenance Using Deep Learning: A video showing how to use deep learning to detect anomalies and monitor equipment condition using MathWorks.
Predictive Maintenance: A Smarter Way to Maintain Assets: A video explaining the benefits and challenges of predictive maintenance, and showcasing a predictive maintenance solution using IBM Watson.

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4.	https://youtu.be/NrI6AOekSKA
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