Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

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Sparse feature extraction based on periodical convolutional sparse representation for fault detection of rotating machinery

Measurement Science and Technology ◽

10.1088/1361-6501/abb0bf ◽

2020 ◽

Vol 32 (1) ◽

pp. 015008

Author(s):

Chuancang Ding ◽

Ming Zhao ◽

Jing Lin

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Sparse Representation ◽

Rotating Machinery

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A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406216632022 ◽

2016 ◽

Vol 231 (6) ◽

pp. 1146-1161 ◽

Cited By ~ 4

Author(s):

Pak Kin Wong ◽

Jian-Hua Zhong ◽

Zhi-Xin Yang ◽

Chi Man Vong

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Extraction Method ◽

Ensemble Empirical Mode Decomposition ◽

Rotating Machinery ◽

Committee Machine ◽

Feature Extraction Method ◽

Diagnostic Framework ◽

The Individual ◽

New Framework

This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneous-fault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as different signal committee members, and each member is trained using vibration or sound signals respectively. The individual diagnostic result from each fault detection member is then combined by a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable fault as compared to individual classifier acting alone. The effectiveness of the proposed framework is verified by a case study on a gearbox fault detection. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single- and simultaneous-faults for the rotating machinery while the framework is trained by single-fault patterns only.

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An early fault detection approach in grid-connected photovoltaic (GCPV) system

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v17.i2.pp671-679 ◽

2020 ◽

Vol 17 (2) ◽

pp. 671

Author(s):

N. Muhammad ◽

H. Zainuddin ◽

E. Jaaper ◽

Z. Idrus

Keyword(s):

Fault Detection ◽

Fire Hazard ◽

Failure Detection ◽

Data Sets ◽

Pv System ◽

Acceptance Ratio ◽

Ac Power ◽

Detection Approach ◽

Detection Stage ◽

Early Fault Detection

<span>Faults in any components of PV system shall lead to performance degradation and if prolonged, it can leads to fire hazard. This paper presents an approach of early fault detection via acquired historical data sets of grid-connected PV (GCPV) systems. The approach is a developed algorithm comprises of failure detection on AC power by using Acceptance Ratio (AR) determination. Specifically, the implemented failure detection stage was based on the algorithm that detected differences between the actual and predicted AC power of PV system. Furthermore, the identified alarm of system failure was a decision stage which performed a process based on developed logic and decision trees. The results obtained by comparing two types of GCPV system (polycrystalline and monocrystalline silicon PV system), showed that the developed algorithm could perceive the early faults upon their occurrence. Finally, when applying AR to the PV systems, the faulty PV system demonstrated 93.38 % of AR below 0.9, while the fault free PV system showed only 31.4 % of AR below 0.9.</span>

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Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery

Energy ◽

10.1016/j.energy.2016.08.039 ◽

2017 ◽

Vol 136 ◽

pp. 63-71 ◽

Cited By ~ 36

Author(s):

Aleksandar Brkovic ◽

Dragoljub Gajic ◽

Jovan Gligorijevic ◽

Ivana Savic-Gajic ◽

Olga Georgieva ◽

...

Keyword(s):

Fault Detection ◽

Rotating Machinery ◽

Fault Detection And Diagnosis ◽

Efficient Operation ◽

Detection And Diagnosis ◽

Early Fault Detection

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Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Entropy ◽

10.3390/e21111061 ◽

2019 ◽

Vol 21 (11) ◽

pp. 1061 ◽

Cited By ~ 6

Author(s):

Liu ◽

Zhi ◽

Zhang ◽

Guo ◽

Peng ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Fault Detection ◽

Condition Monitoring ◽

Quantitative Information ◽

Rotating Machinery ◽

Condition Based Maintenance ◽

Training Data ◽

Sensing Data ◽

Machine Learning Methods

Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance. The potential anomaly, fault and failure information can be obtained by analyzing the collected condition monitoring data of the previously deployed sensors in rotating machinery. Among the available methods of analyzing sensors data, entropy and its variants can provide quantitative information contained in these sensing data. For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods. This article aims to review the related entropy theories which have been applied for condition monitoring of rotating machinery. This review consists of typical entropy theories presentation, application, summary, and discussion.

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Design and Implementation of Acoustic Sensing System for Online Early Fault Detection in Industrial Fans

Journal of Sensors ◽

10.1155/2018/4105208 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 4

Author(s):

Cihun-Siyong Alex Gong ◽

Huang-Chang Lee ◽

Yu-Chieh Chuang ◽

Tien-Hua Li ◽

Chih-Hui Simon Su ◽

...

Keyword(s):

Fault Detection ◽

Real Time ◽

Acoustic Signal ◽

Critical Role ◽

Acoustic Signals ◽

Rotating Machinery ◽

Algorithm Analysis ◽

Time Signal ◽

Early Fault Detection ◽

Physical Sensing

Industrial fans play a critical role in manufacturing facilities, and a sudden shutdown of critical fans can cause significant disruptions. Ensuring early, effective, and accurate detection of fan malfunctions first requires confirming the characteristics of anomalies resulting from initial damage to rotating machinery. In addition, sensing and detection must rely on the use of sensors and sensing characteristics appropriate to various operational abnormalities. This research proposes an online industrial fan monitoring and fault detection technique based on acoustic signals as a physical sensing index. The proposed system detects and assesses anomalies resulting from preliminary damage to rotating machinery, along with improved sensing resolution bandwidth features for microphone sensors as compared to accelerometer sensors. The resulting Intelligent Prediction Integration System with Internet (IPII) is built to analyze rotation performance and predict malfunctions in industrial fans. The system uses an NI cRIO-9065 embedded controller and a real-time signal sensing module. The kernel algorithm is based on an acoustic signal enhancement filter (ASEF) as well as an adaptive Kalman filter (AKF). The proposed scheme uses acoustic signals with adaptive order-tracking technology to perform algorithm analysis and anomaly detection. Experimental results showed that the acoustic signal and adaptive order analysis method could effectively perform real-time early fault detection and prediction in industrial fans.

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On-line automatic early fault detection of rotating machinery

2010 Prognostics and System Health Management Conference ◽

10.1109/phm.2010.5414580 ◽

2010 ◽

Author(s):

Dong Wang ◽

Qiang Miao

Keyword(s):

Fault Detection ◽

Rotating Machinery ◽

On Line ◽

Early Fault Detection

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Gradient Ascent Optimization for Fault Detection in Electrical Power System Based on Wavelet Transformation

Current Signal Transduction Therapy ◽

10.2174/1574362414666190619092910 ◽

2019 ◽

Vol 14 ◽

Author(s):

Iyappan Murugesan ◽

Karpagam Sathish

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Power System ◽

Electrical Power ◽

Daubechies Wavelet ◽

Electrical Power System ◽

Gradient Ascent ◽

Neural Learning ◽

Weight Value ◽

Transmission And Distribution

: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like artificial neural network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the power loss rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TLsignal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, feature extraction accuracy (FEA), and fault detection time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-of-the-art works.

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