Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

This document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.Peer reviewe

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognostic methods lack a fault-identification module to automatically identify the fault root-cause variables required in the subsequent prognostic analysis and decision-making process. The proposed prognostic feature exploring method overcomes this challenge by introducing a canonical variate residuals–based fault-identification method. With this method, the most representative degradation features are extracted from only the fault root-cause variables, thereby facilitating machinery prognostics by ensuring accurate estimates. Its effectiveness is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump. Moreover, an enhanced grey model approach is developed for remaining useful life prediction. In particular, the empirical Bayesian algorithm is employed to improve the traditional grey forecasting model in terms of quantifying the uncertainty of remaining service life in a probabilistic form and improving its prediction accuracy. To demonstrate the superiority of empirical Bayesian–grey model, existing prognostic methods such as grey model, particle filter–grey model and empirical Bayesian–exponential regression are also utilized to realize machinery remaining service life prediction, and the results are compared with that of the proposed method. The achieved predictive accuracy shows that the proposed approach outperforms its counterparts and is highly applicable in fault prognostics of industrial rotating machinery. The use of in-service data in a practical scenario shows that the proposed prognostic approach is a promising tool for online health monitoring

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availability. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have previously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature

Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods