A Modular Fault Diagnosis and Prognosis Method for Hydro-Control Valve System Based on Redundancy in Multisensor Data Information

Kordestani, Mojtaba; Zanj, Amir; Orchard, Marcos E.; Saif, Mehrdad

doi:10.1109/tr.2018.2864706

Cited by 40 publications

(11 citation statements)

References 45 publications

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“…Kernel PCA (KPCA) which is a nonlinear extension of traditional PCA explores the nonlinear relationship among variables in high-dimensional feature spaces by means of integral operator and a nonlinear kernel function for nonlinear process monitoring [37,38]. Kordestani et al proposed a fault diagnosis and prognosis system in [39]. In order to remove re-dundancy in the multisensory data, the feature selection has been produced by Pearson product-moment rank correlation and SVM technique to capture redundant information in the multisensor data.…”

Section: Previous Research On Multiple Features Selection and Indicatmentioning

confidence: 99%

Adaptive event-triggered anomaly detection in compressed vibration data

Zhang

Hutchinson

Lieven

et al. 2019

Mechanical Systems and Signal Processing

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Anomaly detection is a crucial task in Prognostics and Condition Monitoring (PCM) of machinery. In modern remote PCM systems, data compression techniques are regularly used to reduce the need for bandwidth and storage. In these systems the challenge arises of how the compressed (distorted) vibration data affects the condition monitoring algorithms. This paper introduces a novel algorithm that can adaptively establish normal bounds of operation from continuous noisy vibration profiles working with compressed vibration data. The proposed technique is based on four modules, including feature extraction, feature fusion, extreme value vibration modeling and adaptive thresholding for anomaly detection. The proposed method has been validated with experiments using three time-series datasets. The experimental results indicate that the proposed algorithm is able to perform detection of malfunctions in rotating machines effectively without faulty reference data. Moreover, the proposed method is able to produce accurate early warning and alarm indications from both the raw and compressed (distorted) datasets with equal veracity.

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Section: Previous Research On Multiple Features Selection and Indicatmentioning

confidence: 99%

Adaptive event-triggered anomaly detection in compressed vibration data

Zhang

Hutchinson

Lieven

et al. 2019

Mechanical Systems and Signal Processing

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“…Therefore, the driving motor generates a cyclically varying load during operation, thus resulting in high frequency oscillation of the motor current signal. According to Equation (9), the measured RC motor current signal can be expressed [24] as in Equation (10). α l and α r represent angular displacements of the upper and lower side frequencies, respectively.…”

Section: The Model Of Rc Motor Current and Excitation Signal Selectionmentioning

confidence: 99%

“…However, the vibration characteristics of RC have non-linear, non-stationary, and multi-component coupling factors [3,9], which pose challenges for RC fault diagnosis. Generally, the FDM of RC can be divided into three categories: model-based, data-driven, and a combination of the two [2,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…The diagnosis effect of data-driven method mainly depends on the quantity and quality of data and the conditions of collecting data, and it has low requirements for experience knowledge and fault mechanism. Therefore, it has been actively studied in the field of RC fault diagnosis, among which local mean decomposition [1,16], deep confidence network and back-propagation neural network [17][18][19], support vector machine (SVM) [9,20], k approximate regression [18,21], Bayesian estimation algorithm [10,22], big data [23], and other technologies have been successfully applied. The method of combining model and data-driven is to diagnose the system fault by fusing the system operation data with the system fault model.…”

Section: Introductionmentioning

confidence: 99%

“…[37] developed a new numerical simulation method to monitor the RC valves status while using the instantaneous angular speed (IAS) of crankshaft and cylinder pressure. A modular fault diagnosis and prediction method of hydraulic valve system based on multi-sensor data redundancy was proposed in [10]. G. Qi et al [23] used big data and machine learning methods to identify and classify RC faults based on multi-signal such as vibration, temperature, and displacement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

et al. 2020

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High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.

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