Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions

Chen, Peng; Toyota, Toshio; He, Zhengja

doi:10.1109/3468.983436

Cited by 44 publications

(3 citation statements)

References 7 publications

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“…Gasch et al [14] studied the dynamic behavior of cracked rotors. Chen et al [15] conducted extensive research on practical technologies, such as fault mechanism and feature extraction. Sekhar [16] studied the dynamic behavior of rotor cracks and their identification methods.…”

Section: Fault Mechanism and Symptom Relationshipmentioning

confidence: 99%

Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults

Lin

2021

Electronics

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In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition and monitoring system. After analyzing the data and establishing a model, the system can automatically learn the features from the input data to predict the failure of the maintenance and diagnosis equipment, which is important for motor maintenance. This research proposes a medium Gaussian support vector machine (SVM) method for the application of machine learning and constructs a feature space by extracting the characteristics of the vibration signal collected on the spot based on experience. Different methods were used to cluster and classify features to classify motor health. The influence of different Gaussian kernel functions, such as fine, medium, and coarse, on the performance of the SVM algorithm was analyzed. The experimental data verify the performance of various models through the data set released by the Case Western Reserve University Motor Bearing Data Center. As the motor often has noise interference in the actual application environment, a simulated Gaussian white noise was added to the original vibration data in order to verify the performance of the research method in a noisy environment. The results summarize the classification results of related motor data sets derived recently from the use of motor fault detection and diagnosis using different machine learning algorithms. The results show that the medium Gaussian SVM method improves the reliability and accuracy of motor bearing fault estimation, detection, and identification under variable crack-size and load conditions. This paper also provides a detailed discussion of the predictive analytical capabilities of machine learning algorithms, which can be used as a reference for the future motor predictive maintenance analysis of electric vehicles.

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Section: Fault Mechanism and Symptom Relationshipmentioning

confidence: 99%

Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults

Lin

2021

Electronics

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“…Digital twins are virtual representations of physical systems. They are used to monitor systems, predict their behavior and optimize their performance (Chen et al, 2001;El Mejdoubi et al, 2016). Here, the proposed DT is based on a physical model of the brake system solenoid valve and is optimized using a machine learning approach.…”

Section: Introductionmentioning

confidence: 99%

Development of an Operational Digital Twin of a Locomotive Braking System Solenoid Valve for Fault Classification

Davidyan,

Bortman,

Kenett

2023

PHMAP_CONF

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In recent years, a growing role in digital technologies has been filled by model-based digital twinning. A digital twin produces a mapping of a physical structure, operating in the digital domain. Combined with sensor technology and analytics, a digital twin can provide enhanced monitoring, diagnostic, and optimization capabilities. This research harnesses the significant capabilities of digital twining for the unmitigated challenge of fault type classification of a locomotive braking system solenoid valve. We develop a digital twin of the solenoid valve and suggest a method for fault type classification based on the digital twin. The diagnostic ability of the approach is demonstrated on a large experimental dataset.

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“…It not only causes direct baneful influences on performance of equipment and quality of products, but also it causes excessive stress on surrounding components (such as bearings and gears) and causes secondary failure [2]. Therefore, early diagnosis of structural faults and diagnosis of fault types are extremely important subjects [3,4]. Structural faults of rotating machinery often include unbalance, misalignment, and looseness of fasteners, which are caused by machinery structural defects.…”

Section: Introductionmentioning

confidence: 99%

A Precise Diagnosis Method of Structural Faults of Rotating Machinery based on Combination of Empirical Mode Decomposition, Sample Entropy, and Deep Belief Network

Guan

Liao

et al. 2019

Sensors

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To precisely diagnose the rotating machinery structural faults, especially structural faults under low rotating speeds, a novel scheme based on combination of empirical mode decomposition (EMD), sample entropy, and deep belief network (DBN) is proposed in this paper. EMD can decompose a signal into several intrinsic mode functions (IMFs) with different signal-to-noise ratios (SNRs) and sample entropy is performed to extract the signals that carry fault information with high SNR. The extracted fault signal is reconstructed into a new vibration signal that will carry abundant fault information. DBN has strong feature extraction and classification performance. It is suitably performed to build the diagnosis model based on the reconstructed signal. The effectiveness of the proposed method is validated by structural faults signal and the comparative experiments (BPNN, CNN, time-domain signal only, frequency-domain signal only). The results show that the diagnosis accuracy of the proposed method is between 99% and 100%, the BPNN is less than 25%, and the CNN is between 70% and 95%, which means the verified, proposed method has a superior performance to diagnose the structural fault.

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Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions

Cited by 44 publications

References 7 publications

Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults

Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults

Development of an Operational Digital Twin of a Locomotive Braking System Solenoid Valve for Fault Classification

A Precise Diagnosis Method of Structural Faults of Rotating Machinery based on Combination of Empirical Mode Decomposition, Sample Entropy, and Deep Belief Network

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