Bearing Fault Diagnosis Using Frequency Domain Features and Artificial Neural Networks

Jigyasu, Rajvardhan; Mathew, Lini; Chatterji, S.

doi:10.1007/978-981-13-1747-7_52

Cited by 19 publications

(10 citation statements)

References 15 publications

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“…This condition limits the search range of the solution in Eq. (7). Based on the Lagrange multiplier method, the optimization problem shown in Eq.…”

Section: B Mathematical Model For Mining Understandable Fault Informationmentioning

confidence: 99%

“…The second is on the basis of shallow models. The artificial neural network (ANN)-based feature extraction method is the typical [7]. ANN is capable of learning complex non-linear mapping relationship between fault types and condition monitoring data, by the use of its self-learning ability and fault tolerance, which helps researchers or technical staff master the knowledge that is difficult to be expressed in analytical form.…”

Section: Introductionmentioning

confidence: 99%

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Mining of Weak Fault Information Adaptively Based on DNN Inversion Estimation for Fault Diagnosis of Rotating Machinery

2022

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Fault feature extraction plays a significant role in bearing fault diagnosis, especially in incipient fault period. Recently, deep neural network has been favored by many researchers due to its excellent hierarchical feature extraction capabilities. The existing diagnosis methods based on deep neural network mostly take original condition monitoring data as input and further convert fault diagnosis into pattern recognition issues. Although it improves the level of intelligent diagnosis, it is still confronted with practical problems. Since deep neural network includes many non-linear mapping layers, the extracted fault-related features show a high degree of abstraction, which reduces the practical level of this technology. Aimed at this problem, a mining method of understandably weak fault information is proposed based on deep neural network inversion estimation. From perspective of neuron response degree, this method mines the most sensitive input pattern that maximizes the neuron's activation value of network output layer in the original input feature space. It can intuitively mine the weak fault information from the original signal. Two bearing experiments verify the effectiveness and reliability of the proposed method.INDEX TERMS Deep neural network inversion estimation; fault feature extraction; mining of weak fault information.

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“…This condition limits the search range of the solution in Eq. (7). Based on the Lagrange multiplier method, the optimization problem shown in Eq.…”

Section: B Mathematical Model For Mining Understandable Fault Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining of Weak Fault Information Adaptively Based on DNN Inversion Estimation for Fault Diagnosis of Rotating Machinery

2022

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“…To extract useful knowledge and make appropriate decisions from big data, machine learning (ML) techniques have been regarded as a powerful solution. Before going "deep", a variety of "shallow" ML algorithms are developed for the context of PdM, e.g., Artificial Neural Network (ANN) [124][125][126][127][128][129][130][131], decision tree (DT) [132][133][134][135][136], Support Vector Machine (SVM) [137][138][139][140][141], k-Nearest Neighbors (k-NN) [142][143][144][145][146][147], particle filter [148,149], principle component analysis [150,151], adaptive resonance theory [152,153], self-organizing maps [154,155], etc. In this section, a subset of well-developed ML algorithms are reviewed and briefly summarized, with a complete list of references.…”

Section: Traditional Machine Leaning Based Approachesmentioning

confidence: 99%

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

Ran,

Zhou,

Lin

et al. 2019

Preprint

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This paper provides a comprehensive literature review on Predictive Maintenance (PdM) with emphasis on system architectures, purposes and approaches. In industry, any outages and unplanned downtime of machines or systems would degrade or interrupt a company's core business, potentially resulting in significant penalties and unmeasurable reputation loss. Existing traditional maintenance approaches suffer from some assumptions and limits, such as high prevent/repair costs, inadequate or inaccurate mathematical degradation processes and manual feature extraction. With the trend of smart manufacturing and development of Internet of Things (IoT), data mining and Artificial Intelligence (AI), etc., PdM is proposed as a novel type of maintenance paradigm to perform maintenances only after the analytical models predict certain failures or degradations. In this survey, we first provide a high-level view of the PdM system architectures including the Open System Architecture for Condition Based Monitoring (OSA-CBM), cloud-enhanced PdM system and PdM 4.0, etc. Then, we make clear the specific maintenance purposes/objectives, which mainly comprise cost minimization, availability/reliability maximization and multiobjective optimization. Furthermore, we provide a review of the existing approaches for fault diagnosis and prognosis in PdM systems that include three major subcategories: knowledge based, traditional Machine Learning (ML) based and DL based approaches. We make a brief review on the knowledge based and traditional ML based approaches applied in diverse industrial systems or components with a complete list of references, while providing a comprehensive review of DL based approaches. Finally, important future research directions are introduced.

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“…There are several fault Classification techniques are followed now a day's which are SVM [8,9], Artificial neural network [10,11], Fuzzy [12], KNN [13,14], Ensembles [15] and several more algorithms. This section of the paper explains about the need of IM in the industries, different type of faults their detection and classification techniques, different signal processing techniques used in analysis as well as need of fault detection and diagnosis.…”

Section: Fig1 Classification Of Signal Processing Techniquesmentioning

confidence: 99%

Smart Classifiers Based Classification and Condition Monitoring of Induction Motor Faults

Chikkam*¹,

Singh²,

Jigyasu³

2019

IJITEE

Self Cite

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With the advancements in the field of automation in the industries the use of machines is very high and if the machine which require some rotatory action for the load, the Induction motor comes in to play because of the advantages such as robustness, low maintenance, low cost etc. But with the increase in the dependency over the motors it becomes highly recommended to have machines with reliability because break in the work can lead to huge amount of loss. In order to increase the reliability of the motors predictive maintenance comes into play which requires fault classification or detection which is easily and accurately possible using the Machine Learning algorithms. With the requirements of the present scenario for predictive maintenance, this paper presents the fault classification of induction motor using Support Vector Machine SVM) and K- Nearest Neighbour (KNN) technique of classification. Here in this paper the bearing fault (BF) and broken rotor bar (BRB) fault is considered. The results collected are on the basis of validation and Principle Component Analysis (PCA) technique. And it is found that the SVM technique is better than the KNN for fault classification of Induction motor.

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Bearing Fault Diagnosis Using Frequency Domain Features and Artificial Neural Networks

Cited by 19 publications

References 15 publications

Mining of Weak Fault Information Adaptively Based on DNN Inversion Estimation for Fault Diagnosis of Rotating Machinery

Mining of Weak Fault Information Adaptively Based on DNN Inversion Estimation for Fault Diagnosis of Rotating Machinery

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

Smart Classifiers Based Classification and Condition Monitoring of Induction Motor Faults

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