Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

Hamadache, Moussa; Lee, Dongik; Mucchi, Emiliano; Dalpiaz, Giorgio

doi:10.3390/app8081392

Cited by 20 publications

(17 citation statements)

References 36 publications

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“…model-based techniques. These include the fact that the accuracy of the developed model directly affects FDD process performance, and that the construction of high-fidelity mathematical models from physical principles of such a complex system (i.e., a railway S&C system) can become very complicated, time-consuming, and even sometimes unfeasible [85], in addition to the need for much prior knowledge about real systems before model development. Compared with model-based FDD techniques, data-driven FDD methods are generally more practical, in that there is no need to build a reference model (i.e., less prior knowledge required, no need for model accuracy validation to be applied for real-world applications, etc.…”

Section: Data-driven Fdd Methods For Railway Sandc Systemsmentioning

confidence: 99%

“…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]. These techniques can be used separately or in combination (e.g., hybrid techniques).…”

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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Section: Data-driven Fdd Methods For Railway Sandc Systemsmentioning

confidence: 99%

Section: Fd Methodsmentioning

confidence: 99%

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

et al. 2019

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“…The analysis of the vibration signal is an important approach for monitoring the running state of mechanical equipment and the particularly significant task is to extract the fault feature frequency from the complex vibration signal accurately [ 11 , 12 , 13 ]. For the early failure of mechanical system, it is in the germination stage and the difference between its performance and normal state is small.…”

Section: Introductionmentioning

confidence: 99%

Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis

Zhang

2018

Entropy

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The characteristics of the early fault signal of the rolling bearing are weak and this leads to difficulties in feature extraction. In order to diagnose and identify the fault feature from the bearing vibration signal, an adaptive local iterative filter decomposition method based on permutation entropy is proposed in this paper. As a new time-frequency analysis method, the adaptive local iterative filtering overcomes two main problems of mode decomposition, comparing traditional methods: modal aliasing and the number of components is uncertain. However, there are still some problems in adaptive local iterative filtering, mainly the selection of threshold parameters and the number of components. In this paper, an improved adaptive local iterative filtering algorithm based on particle swarm optimization and permutation entropy is proposed. Firstly, particle swarm optimization is applied to select threshold parameters and the number of components in ALIF. Then, permutation entropy is used to evaluate the mode components we desire. In order to verify the effectiveness of the proposed method, the numerical simulation and experimental data of bearing failure are analyzed.

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“…Principal component analysis (PCA) [41][42][43]-PCA extracts k principal components by using a linear transformation of the singular value decomposition (SVD) to maintain most of the variability in input data. • Latent semantic analysis (LSA) [44]-Contrary to the PCA, LSA performs the linear dimensionality reduction by means of the truncated SVD.…”

mentioning

confidence: 99%

“…and 'RFE algo.,' only the original variables are used as input variables in the learning algorithm. Dimension-reduction by PCA-Before executing the learning method, the dimension of the input space consisting of all of the newly constructed features and the original variables can be finally reduced by the PCA [41][42][43]. The 'PCA flag' and 'PCA para.'…”

mentioning

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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Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

Cited by 20 publications

References 36 publications

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

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

Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis

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

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