An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM

doi:10.5545/sv-jme.2018.5249

Cited by 37 publications

(28 citation statements)

References 21 publications

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“…Non-parametric methods are also very useful tools for classification purposes. We have selected methods which are, in our opinion, most commonly used for engineering purposes [6] and [15]. In the following paragraphs brief explanations of different classification methods are given.…”

Section: Non-parametric Classification Methodsmentioning

confidence: 99%

“…The bearing used for testing was ER16K ball bearing. In previous studies [6], [15] and [49] a plethora of features that could be extracted from the vibrational data were studied, specifically from the time domain, frequency domain or the timefrequency domain using various signal-processing tools such as the Fourier transform, Hilbert transform, Wavelet transform, etc. The feature-extraction part can greatly enhance the results of the classification and there is a lot of studies emerging on this topic [50].…”

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

“…However, since this paper represents the application of a classification method and its variants to one of the most dominant problems in the field of bearing and rotating-machinery fault detection we will simplify the feature-extraction process to only the statistical features of the vibrational signals in the time and frequency domains. This resulted in thirteen different most popular statistical features, judging by the literature [6], [15] and [49]. Features are given in Table 3, where x i is the i th amplitude of the acceleration signal, N is the number of samples in the signal, μ x is the mean value of the signal, σ x is the standard deviation of the signal, f i is the corresponding i th frequency amplitude.…”

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

“…Another great example of the utilization of the classification methods is the bearing-fault classification [6] and [7]. Bearing-fault detection is a very popular problem in mechanical engineering since bearings are one of the most utilized rotational mechanical elements [8] and [9].…”

Section: Introductionmentioning

confidence: 99%

“…The first type of studies covers different signal-processing techniques for the classification of bearing faults [7] and [13] or for feature extraction and selection from vibrational data, which are then used to enhance the results of an applied classification method [14]. Other studies mostly utilize the different classification methods to obtain better classification results [6] and [15]. This paper is of the latter type.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

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Section: Non-parametric Classification Methodsmentioning

confidence: 99%

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

View full text Add to dashboard Cite

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Bearing Fault Diagnosis Based on Prominence Peak-Picking IMFs Selection and PSO-SSAE

Saufi,

Leong,

Hee

et al. 2024

Lecture Notes in Networks and Systems

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Fault diagnosis and self-healing for smart manufacturing: a review

Aldrini,

Chihi,

Sidhom

2023

J Intell Manuf

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Manufacturing systems are becoming more sophisticated and expensive, particularly with the development of the intelligent industry. The complexity of the architecture and concept of Smart Manufacturing (SM) makes it vulnerable to several faults and failures that impact the entire behavior of the manufacturing system. It is crucial to find and detect any potential anomalies and faults as soon as possible because of the low tolerance for performance deterioration, productivity decline, and safety issues. To overcome these issues, a variety of approaches exist in the literature. However, the multitude of techniques make it difficult to choose the appropriate method in relation to a given context. This paper proposes a new architecture for a conceptual model of intelligent fault diagnosis and self-healing for smart manufacturing systems. Based on this architecture, a review method for the different approaches, sub-approaches and methods used to develop a Fault Detection and Diagnosis (FDD) and Self-Healing-Fault-Tolerant (SH-FT) strategy dedicated to smart manufacturing is defined. Moreover, this paper reviews and analyzes more than 256 scientific articles on fault diagnosis and self-healing approaches and their applications in SM in the last decade. Finally, promising research directions in the field of resilient smart manufacturing are highlighted.

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An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM

Cited by 37 publications

References 21 publications

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Bearing Fault Diagnosis Based on Prominence Peak-Picking IMFs Selection and PSO-SSAE

Fault diagnosis and self-healing for smart manufacturing: a review

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