A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

Hamadache, Moussa; Jung, Joon Ha; Park, Jung Ho; Youn, Byeng D.

doi:10.1007/s42791-019-0016-y

Cited by 121 publications

(58 citation statements)

References 194 publications

(266 reference statements)

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“…As shown in Figure 1a, we first outlined the general process of the data-driven diagnostics and prognostics, which consists of four subtasks of data rebalancing (for classification problems), feature extraction, feature reduction, and learning. We did not explicitly include a feature selection in our framework, although it is a frequently used technique [48]. In fact, an implicit feature selection was already employed in the feature extraction stage because the inclusion and exclusion of created features are dynamically determined by a chromosome in the genetic algorithm (see the Section 3.1.1 for more details).…”

Section: The Proposed Methodsmentioning

confidence: 99%

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Trinh

Kwon

2020

Applied Sciences

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Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.

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Section: The Proposed Methodsmentioning

confidence: 99%

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Trinh

Kwon

2020

Applied Sciences

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“…With promising results for RUL prediction of 10 real S&C systems, Güçlü et al [84] proposed a model-based prognostic technique based on an autoregressive moving-average (ARMA) model. Tension, compression forces, DC current, voltage in electric circuits, distance between the stock rail and switch rail of railway turnout systems, and the linear position of the switch rails were measured for both normal to reverse and reverse to normal movements of S&C systems.…”

Section: Fp Methodsmentioning

confidence: 99%

“…From the above reviewed literature, the lack of papers applying a model-based FDD method for S&C systems is obvious. Further, very few among these published papers [12,76,84] actually applied and used the residuals principle for detecting and then diagnosing S&C system faults. Thus, there is an opportunity for researchers to consider applying model-based methods to deal with FD, FDD, and/or FP of the different possible fault modes (previously shown in Figure 9) in railway S&C…”

Section: Fp Methodsmentioning

confidence: 99%

“…Adachi et al [88] applied three data-mining techniques (i.e., hypothesis testing, discriminant Data-driven techniques are gaining in popularity since they are -as can be seen in Figure 13-model-free methods. Several features (indices, criteria) are generated in the time, frequency, time-frequency domain, and/or envelope spectrum features, which can usually be physically interpreted, or statistical features, which cannot be physically interpreted [84]. Different techniques from different disciplines are commonly used for data-driven FDD, including techniques from (digital) signal processing, cluster analysis, data mining, statistical pattern recognition, modern artificial intelligence (i.e., machine learning and deep learning), and image processing [84,85].…”

Section: Fd Methodsmentioning

confidence: 99%

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On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

et al. 2019

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Railway switch and crossing (S&C) systems have a very complex structure that requires not only a large number of components (such as rails, check rails, switches, crossings, turnout bearers, slide chair, etc.) but also different types of components and technologies (mechanical devices to operate switches, electrical and/or electronic devices for control, etc.). This complexity of railway S&C systems makes them vulnerable to failures and malfunctions that can ultimately cause delays and even fatal accidents. Thus, it is crucial to develop suitable condition monitoring techniques to deal with fault detection and diagnosis (FDD) in railway S&C systems. The main contribution of this paper is to present a comprehensive review of the existing FDD techniques for railway S&C systems. The aim is to overview the state of the art in rail S&C and in doing so to provide a platform for researchers, railway operators, and experts to research, develop and adopt the best methods for their applications; thereby helping ensure the rapid evolution of monitoring and fault detection in the railway industry at a time of the increased interest in condition based maintenance and the use of high-speed trains on the rail network.

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“…An ensemble of DCNNs for bearing fault diagnosis and an improved Dempster-Shafer theory (IDSCNN) was described [24]. The proposed model has a high diagnosis accuracy and adaptability when compared with SVM, multi-layer perceptron neural network (MLP), DNN, DCNN with wide first-layer kernels (WDCNN) [25] and DSCNN models.…”

Section: Related Workmentioning

confidence: 99%

Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

2020

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Performance assessment and timely failure detection of the electric submersible pump can reduce operation costs and maintenance in the oil and gas field. Features of equipment malfunction are changes in vibration signals. Evaluation of vibrations based on accelerometer sensors can detect failures and allows assessment of system failures. This paper proposes a reliable deep learning-based method for electric submersible pump faults detection. The frequency, time and spectral information of the vibrational signal are considered as input to the deep hybrid model. The spectral information includes the spectrogram obtained using the short-time Fourier transform and the scalogram as a result of the continuous wavelet transform and provides a detailed study of the vibration signal. The proposed approach is compared with k-nearest neighbors, support vector machines, logistic regression, and random forest. The experimental evaluation shows that the proposed deep hybrid model is superior to these machine learning methods, and can automatically and simultaneously detect failures of the electric submersible pump according to the vibration signal that is generated during system operation. The proposed approach gives good results and can help an expert in automatic diagnostics of equipment and several complex technical systems.

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A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

Cited by 121 publications

References 194 publications

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

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