This paper provides a comprehensive literature review on Predictive Maintenance (PdM) with emphasis on system architectures, purposes and approaches. In industry, any outages and unplanned downtime of machines or systems would degrade or interrupt a company's core business, potentially resulting in significant penalties and unmeasurable reputation loss. Existing traditional maintenance approaches suffer from some assumptions and limits, such as high prevent/repair costs, inadequate or inaccurate mathematical degradation processes and manual feature extraction. With the trend of smart manufacturing and development of Internet of Things (IoT), data mining and Artificial Intelligence (AI), etc., PdM is proposed as a novel type of maintenance paradigm to perform maintenances only after the analytical models predict certain failures or degradations. In this survey, we first provide a high-level view of the PdM system architectures including the Open System Architecture for Condition Based Monitoring (OSA-CBM), cloud-enhanced PdM system and PdM 4.0, etc. Then, we make clear the specific maintenance purposes/objectives, which mainly comprise cost minimization, availability/reliability maximization and multi-objective optimization. Furthermore, we provide a review of the existing approaches for fault diagnosis and prognosis in PdM systems that include three major subcategories: knowledge based, traditional Machine Learning (ML) based and DL based approaches. We make a brief review on the knowledge based and traditional ML based approaches applied in diverse industrial systems or components with a complete list of references, while providing a comprehensive review of DL based approaches. Finally, important future research directions are introduced.

pdf/a-survey-of-predictive-maintenance-systems-purposes-and-1krz2ktem8.pdf

A Survey of Predictive Maintenance: Systems, Purposes and Approaches.

Predictive Maintenance (PdM) is a prominent strategy comprising all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the main challenges of PdM is to design and develop an embedded smart system to monitor and predict the health status of the machine. In this work, we use a data-driven approach based on machine learning applied to woodworking industrial machines for a major woodworking Italian corporation. Predicted failures probabilities are calculated through tree-based classification models (Gradient Boosting, Random Forest and Extreme Gradient Boosting) and calculated as the temporal evolution of event data. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime (RUL) of woodworking machines. The effectiveness of the proposed method is showed by testing an independent sample of additional woodworking machines without presenting machine down. The Gradient Boosting model achieved accuracy, recall, and precision of 98.9%, 99.6%, and 99.1%. Our predictive maintenance approach deployed on a Big Data framework allows screening simultaneously multiple connected machines by learning from terabytes of log data. The target prediction provides salient information which can be adopted within the maintenance management practice.

SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0

Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review

We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazards of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.

Deep Survival Machines : Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks

We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.

Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks

Predictive maintenance techniques are designed to help anticipate equipment failures to allow for advance scheduling of corrective maintenance, thereby preventing unexpected equipment downtime, improving service quality for customers, and also reducing the additional cost caused by over-maintenance in preventative maintenance policies. Many types of equipment--e.g., automated teller machines (ATMs), information technology equipment, medical devices, etc.--track run-time status by generating system messages, error events, and log files, which can be used to predict impending failures. Aiming at these types of equipment, we present a general classification-based failure prediction method. In our parameterized model, we systematically defined four categories of features to try to cover all possibly useful features, and then used feature selection to identify the most important features for model construction. The general solution is sufficiently flexible and complex to address failure prediction for target equipment types. We chose ATMs as the example equipment and used real ATM run-time event logs and maintenance records as experimental data to evaluate our method on the feasibility and effectiveness. In this paper, we also share insights on how to optimize the model parameters, select the most effective features, and tune classifiers to build a high-performance prediction model.

Predictive maintenance based on event-log analysis: a case study

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Predictive maintenance based on event-log analysis: a case study