Fault detection analysis in rolling element bearing: A review

Gupta, Pankaj; Pradhan, Mohan Kumar

doi:10.1016/j.matpr.2017.02.054

Cited by 104 publications

(59 citation statements)

References 29 publications

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“…The diagnostic process relies on an explicit model of the normal system behavior, its structure, and/or its known faults. Currently, most of the model-based methods for spall size estimation are based on frequency and/or time domain analysis of the acceleration signal [6]. Jena and Panigrahi use a continuous wavelet transform on the vibration signal to locate the entry and exit of the bearing in and out of the defect for determination of the passage period of the ball [7].…”

Section: Model-based Approachesmentioning

confidence: 99%

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Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes

Tam

Kalech

Rokach

et al. 2020

Sensors

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Bearing spall detection and predicting its size are great challenges. Model-based simulation is a well-known traditional approach to physically model the influence of the spall on the bearing. Building a physical model is challenging due to the bearing complexity and the expert knowledge required to build such a model. Obviously, building a partial physical model for some of the spall sizes is easier. In this paper, we propose a machine-learning algorithm, called Probability-Based Forest, that uses a partial physical model. First, the behavior of some of the spall sizes is physically modeled and a simulator based on this model generates scenarios for these spall sizes in different conditions. Then, the machine-learning algorithm trains these scenarios to generate a prediction model of spall sizes even for those that have not been modeled by the physical model. Feature extraction is a key factor in the success of this approach. We extract features using two traditional approaches: statistical and physical, and an additional new approach: Time Series FeatuRe Extraction based on Scalable Hypothesis tests (TSFRESH). Experimental evaluation with well-known physical model shows that our approach achieves high accuracy, even in cases that have not been modeled by the physical model. Also, we show that the TSFRESH feature-extraction approach achieves the highest accuracy.

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Section: Model-based Approachesmentioning

confidence: 99%

“…A bearing may suffer from different failure modes such as corrosion, overheating, brinelling, contamination, etc. The most common failure mode is spalling [6]. Spalling is a process of surface deterioration in which a splinter or chip detaches and is removed from the surface of a larger body ( Figure 2).…”

Section: Background and Problem Definitionmentioning

confidence: 99%

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes

Tam

Kalech

Rokach

et al. 2020

Sensors

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“…Time-domain analysis, which usually involves the scalar indices to determine bearing condition, is the most straightforward technique for detecting and diagnosing faults in bearings [16]. In this analysis, the signal is studied through temporal vibrational signal data, and based on its value, the bearing condition can be estimated.…”

Section: Introductionmentioning

confidence: 99%

“…The FFT and discrete Fourier transform (DFT) algorithms are used to analyze the raw vibration signal in the frequency-domain. The advantage of frequency-domain over the time-domain analysis is that the frequency-domain method can detect the required specific frequency components effortlessly [16]. The bearing vibration signals are the combination of periodic components, which are cyclic, time invariance and non-stationary, leading to the development of time-frequency analysis approaches.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset. INDEX TERMS Bearing, deep learning, machine learning, machinery fault detection and diagnosis, CWRU dataset.

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“…Many researchers are committed to developing early fault diagnosis (EFD) techniques to monitor the health conditions of rotating machinery [17][18][19][20][21]. At present, many EFD techniques have been successfully applied in the modern industries, such as the vibration-based EFD method [22], current-based EFD method [23], acoustic emission-based EFD method [24], sound-based EFD method [25], torque-based EFD method [26], and rotating encoder-based EFD method [27], etc.…”

Section: Introductionmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

174

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Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

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Fault detection analysis in rolling element bearing: A review

Cited by 104 publications

References 29 publications

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

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