A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems

The rolling bearings are one of the most critical components in rotary machinery. To prevent unexpected bearing failure, it is crucial to develop the effective fault detection and diagnosis techniques to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new fault detection and diagnosis method based on Wigner-Ville spectrum entropy (WVSE) is proposed. First, the local mean decomposition (LMD) and the Wigner-Ville distribution (WVD) are combined to develop a new feature extraction approach to extract the fault features in time-frequency domain of the bearing vibration signals. Second, the concept of the Shannon entropy is integrated into the WVD to define the Wigner-Ville spectrum entropy to quantify the energy variation in time-frequency distribution under different work conditions. The research results from the bearing vibration signals demonstrate that the proposed method based on WVSE can identify different fault patterns more accurately and effectively comparing with other methods based on singular spectrum entropy (SSE) or power spectrum entropy (PSE).

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Method on the Fault Detection and Diagnosis for the Railway Turnout Based on the Current Curve of Switch Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1022 ◽

2013 ◽

Vol 427-429 ◽

pp. 1022-1027 ◽

Cited By ~ 2

Author(s):

Xue Mei Mo ◽

Yu Fang ◽

Yun Guo Yang

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Fault Location ◽

Fault Detection And Diagnosis ◽

Curve Matching ◽

Reference Curve ◽

Current Curve ◽

Diagnosis Method ◽

Detection And Diagnosis ◽

Railway Turnout

This paper proposes a method of the fault detection and diagnosis for the railway turnout based on the current curve of switch machine. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted in the paper. Based on envelope and morpheme match algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Moreover, the SVM-based fault diagnosis method is used to make sure that the fault conditions could be diagnosed intelligently. Finally, the experimental results show that the proposed method can accurately identify the turnout fault status in the conversion process, and the accuracy rate in the diagnosis of the fault location is above 98%, which verify the effectiveness of the method in the fault detection and diagnosis.

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Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

Computational Intelligence and Neuroscience ◽

10.1155/2019/5367217 ◽

2019 ◽

Vol 2019 ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Jingjing Liu ◽

Min Zhang ◽

Hai Wang ◽

Wei Zhao ◽

Yan Liu

Keyword(s):

Fault Detection ◽

Clustering Analysis ◽

Fault Detection And Diagnosis ◽

Sensor Faults ◽

Control Loop ◽

One Dimensional ◽

Air Handling Unit ◽

Sensor Fault Detection ◽

Diagnosis Method ◽

Detection And Diagnosis

This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature (Tsup) control loop of the air handling unit. In this approach, 1-D CNN is employed to extract man-guided features from raw data, and the extracted features are analyzed by WaveCluster clustering. The suspicious sensor faults are indicated and categorized by denoting clusters. Moreover, the Tc acquittal procedure is introduced to further improve the accuracy of FDD. In validation, false alarm ratio and missing diagnosis ratio are mainly used to demonstrate the efficiency of the proposed FDD method. Results show that the abrupt sensor faults in Tsup control loop can be efficiently detected and diagnosed, and the proposed method is equipped with good robustness within the noise range of 6 dBm∼13 dBm.

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