scholarly journals Analysis of the feasibility of an array of MEMS microphones to machinery condition monitoring or fault diagnosis

2017 ◽  
Vol 141 (5) ◽  
pp. 3958-3958
Author(s):  
Lara del-Val ◽  
Alberto Izquierdo ◽  
Juan J. Villacorta ◽  
Luis Suarez ◽  
Marta Herráez
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Machinery Condition Monitoring ◽  
Mems Microphones

scholarly journals First steps on fan matrix condition monitoring and fault diagnosis using an array of digital MEMS microphones

2017 ◽  
Author(s):  
Lara del Val ◽  
Alberto Izquierdo ◽  
Juan J. Villacorta ◽  
Luis Suárez ◽  
Marta Herráez
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Mems Microphones ◽  
Matrix Condition

Dissimilarity Measures for ICA-Based Source Number Estimation

2012 ◽  
Author(s):  
Wei Cheng ◽  
Seungchul Lee ◽  
Zhousuo Zhang ◽  
Zhengjia He
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
A Priori ◽  
Source Separation ◽  
Mechanical Vibration ◽  
Estimation Method ◽  
Dissimilarity Measures ◽  
Source Number Estimation ◽  
Machinery Condition Monitoring ◽  
Source Number

Most of blind source separation problems are carried out with a priori knowledge of the source numbers. However, for source separation-based machinery condition monitoring and fault diagnosis, it is a challenge work to determine the number of sources for a well source separation due to complex structures and nonlinear mixing mode. Therefore, source number estimation is a necessary and important procedure prior to source separation and further diagnosis work. In this paper, we focus on a novel source number estimation method based on independent component analysis (ICA) and clustering evaluation analysis, and investigate the performances of different dissimilarity measures of ICA-based source number estimations with typical mechanical vibration signals. Our work contributes to find an effective solution of source number estimation for source separation-based machinery condition monitoring and fault diagnosis.

Study on Remote Condition Monitoring and Fault Diagnosis System for Rotating Machinery Based on LabVIEW

2012 ◽  
Vol 430-432 ◽  
pp. 1939-1942 ◽  
Author(s):  
Chuan Hui Wu ◽  
Yan Gao ◽  
Yu Guo
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Signal Acquisition ◽  
Diagnosis System ◽  
Time Frequency ◽  
Machinery Condition Monitoring ◽  
Temperature Signal ◽  
Fault Diagnosis System

In order to suit the demand of monitoring and fault diagnosis of modern small and medium machinery devices better, this paper discusses the development of machinery condition monitoring and fault diagnosis system of good universality and strong expansibility using LabVIEW. Mainly illuminates vibration signal, temperature signal and electric current signal acquisition module using NI data acquisition hardware; signal analysis module in time domain, frequency domain and joint time–frequency domain using signal processing technology. DataSocket, database and fuzzy diagnosis technique have been utilized enabling this system to monitor and diagnose machinery fault remotely.

scholarly journals Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

2021 ◽  
pp. 095440622110165
Author(s):  
Zhang Chao ◽  
Wang Wei-zhi ◽  
Zhang Chen ◽  
Fan Bin ◽  
Wang Jian-guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Short Term Memory ◽  
Vibration Signal ◽  
Short Term ◽  
Global Features ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

Automatic Condition Monitoring and Early Fault Detection of Gearbox

2009 ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Chengdong Wang ◽  
Jingqi Xiong
Keyword(s):  
Condition Monitoring ◽  
Wavelet Packet ◽  
Life Time ◽  
Health Condition ◽  
Evaluation Index ◽  
Dynamic Threshold ◽  
Time Data ◽  
Machinery Condition Monitoring ◽  
Maintenance Program ◽  
Gear Fault

Condition based maintenance (CBM) improves decision-making performances for a maintenance program through machinery condition monitoring. Therefore, it is a key step to trace machinery health condition for CBM. In this paper, a novel method is proposed to establish a health evaluation index named automatic evaluation index (AEI) and its corresponding dynamic threshold using Wavelet Packet Transform (WPT) and Hidden Markolv Model (HMM). In this process, WPT is used to decompose signal into detail signals and exhibits prominent gear fault features. In addition, HMM employed here is to recognize two concerned states of gear in the whole life validation, including normal gear state and early gear fault state. It is also important to build a dynamic threshold to differentiate the two states automatically. The proposed dynamic threshold not only renews by itself according to the history values of AEI but also easily and automatically detects occurrence of gear early fault. Finally, a set of whole life time data ending in gear failure is used to verify the proposed method effectively. Further, some related parameters included in this method are discussed and the obtained results show that condition monitoring performance of the proposed method is excellent in detection of gear failure.

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