A Novel Fault Detection and Diagnosis Method Based on Gaussian-Bernoulli Restricted Boltzmann Machine

2019 ◽  
Author(s):  
Peng Peng ◽  
Yinan Wu ◽  
Yi Zhang ◽  
Heming Zhang
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine ◽  
Diagnosis Method ◽  
Detection And Diagnosis

A NewFault Detection and Diagnosis Method Based on Wigner-Ville Spectrum Entropy for the Rolling Bearing

2012 ◽  
Vol 197 ◽  
pp. 346-350 ◽  
Author(s):  
Ping Xie ◽  
Yu Xin Yang ◽  
Guo Qian Jiang ◽  
Yi Hao Du ◽  
Xiao Li Li
Keyword(s):  
Fault Detection ◽  
Rolling Bearing ◽  
Work Conditions ◽  
Fault Detection And Diagnosis ◽  
Vibration Signals ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
Detection And Diagnosis ◽  
Critical Components

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).

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

2013 ◽  
Vol 427-429 ◽  
pp. 1022-1027 ◽  
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.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
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.

scholarly journals Fault-Tolerant SRM Drive with a Diagnosis Method Based on the Entropy Feature Approach

2020 ◽  
Vol 10 (10) ◽  
pp. 3516 ◽  
Author(s):  
Vitor F. Pires ◽  
Tito G. Amaral ◽  
Armando Cordeiro ◽  
Daniel Foito ◽  
Armando J. Pires ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
High Reliability ◽  
Power Converter ◽  
Fault Detection And Diagnosis ◽  
Switched Reluctance ◽  
Power Semiconductors ◽  
Power Semiconductor ◽  
Diagnosis Method ◽  
Detection And Diagnosis

The power electronic converter design is essential for the operation of the switched reluctance motor (SRM). Thus, a fault-tolerant power converter is fundamental to ensure high reliability and extend the drive operation. To achieve fault tolerance, fault detection and diagnosis methods are critical in order to identify, as soon as possible, the failure mode of the drive. To provide such capability, it is proposed in this paper a new fault-tolerant power converter scheme combined with a fault detection method regarding the most common power semiconductors failures in SRM drives. The fast and reliable proposed diagnosis method is based on the entropy theory. Based on this theory, normalized indexes (diagnostic variables) are created, which are independent from the load and speed of the motor. Through this method, it is possible to identify the faulty leg, as well as the type of power semiconductor fault. To test and evaluate the proposed solution several laboratory experiments were carried out using a 2 kW four-phase 8 / 6 SRM.

Turbine Engine Modeling Using Temporal Neural Networks for Incipient Fault Detection and Diagnosis

2004 ◽  
Author(s):  
Sunil Menon ◽  
O¨nder Uluyol ◽  
Deepanker Gupta
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Sensor Data ◽  
Turbine Engine ◽  
Echo State Networks ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Detection And Diagnosis ◽  
Incipient Fault Detection

We present a method of fault detection and diagnosis in turbine engines using temporal neural networks. Temporal neural networks allow us to represent the complete engine operating range by complementing the first-principle models which are usually restricted to takeoff and cruise phases. Because faults that are manifest only in particular phases can be detected, complete coverage leads to more accurate anomaly detection and fault diagnosis systems. The time series sensor data from the engine is collected during particular aircraft flight phases such as startup, takeoff, cruise, and shutdown. We use the echo state network to develop an incipient fault detection and diagnosis system. Echo state networks have several advantages over conventional types of temporal neural networks, including accuracy and ease of training. We demonstrate the efficacy of using the echo state networks to focus on flight phases that are difficult to model. We present results of our fault detection and diagnosis method with actual propulsion engine transient flight data.

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