Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell

2014 ◽  
Vol 18 ◽  
pp. 1-8 ◽  
Author(s):  
M.S. Safizadeh ◽  
S.K. Latifi
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Load Cell ◽  
Sensor Data ◽  
Rolling Element Bearings ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Rolling Element

scholarly journals On the Accuracy of Fault Diagnosis for Rolling Element Bearings Using Improved DFA and Multi-Sensor Data Fusion Method

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6465 ◽  
Author(s):  
Qiang Song ◽  
Sifang Zhao ◽  
Mingsheng Wang
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Classification Accuracy ◽  
Sensor Data ◽  
Fluctuation Analysis ◽  
Rolling Element Bearings ◽  
Linear Discriminant ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Rolling Element

Rolling element bearings are widely employed in almost every rotating machine. The health status of bearings plays an important role in the reliability of rotating machines. This paper deals with the principle and application of an effective multi-sensor data fusion fault diagnosis approach for rolling element bearings. In particular, two single-axis accelerometers are employed to improve classification accuracy. By applying the improved detrended fluctuation analysis (IDFA), the corresponding fluctuations detrended by the local fit of vibration signals are evaluated. Then the polynomial fitting coefficients of the fluctuation function are selected as the fault features. A multi-sensor data fusion classification method based on linear discriminant analysis (LDA) is presented in the feature classification process. The faults that occurred in the inner race, cage, and outer race are considered in the paper. The experimental results show that the classification accuracy of the proposed diagnosis method can reach 100%.

Research on Equipment Fault Diagnosis Method Based on Multi-Sensor Data Fusion

2012 ◽  
Vol 466-467 ◽  
pp. 1222-1226
Author(s):  
Bin Ma ◽  
Lin Chong Hao ◽  
Wan Jiang Zhang ◽  
Jing Dai ◽  
Zhong Hua Han
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Diagnostic Accuracy ◽  
Evidence Theory ◽  
Experimental Results ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Sensor

In this paper, we presented an equipment fault diagnosis method based on multi-sensor data fusion, in order to solve the problems such as uncertainty, imprecision and low reliability caused by using a single sensor to diagnose the equipment faults. We used a variety of sensors to collect the data for diagnosed objects and fused the data by using D-S evidence theory, according to the change of confidence and uncertainty, diagnosed whether the faults happened. Experimental results show that, the D-S evidence theory algorithm can reduce the uncertainty of the results of fault diagnosis, improved diagnostic accuracy and reliability, and compared with the fault diagnosis using a single sensor, this method has a better effect.

Investigation of a multi-sensor data fusion technique for the fault diagnosis of gearboxes

2019 ◽  
Vol 233 (13) ◽  
pp. 4764-4775 ◽  
Author(s):  
Jun He ◽  
Shixi Yang ◽  
Evangelos Papatheou ◽  
Xin Xiong ◽  
Haibo Wan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Sensor Data ◽  
Health Conditions ◽  
Mechanical Transmission ◽  
Selection Scheme ◽  
Vibration Signals ◽  
Fusion Technique ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

Gearbox is the key functional unit in a mechanical transmission system. As its operating condition being complex and the interference transmitting from diverse paths, the vibration signals collected from an individual sensor may not provide a fully accurate description on the health condition of a gearbox. For this reason, a new method for fault diagnosis of gearboxes based on multi-sensor data fusion is presented in this paper. There are three main steps in this method. First, prior to feature extraction, two signal processing methods, i.e. the energy operator and time synchronous averaging, are applied to multi-sensor vibration signals to remove interference and highlight fault characteristic information, then the statistical features are extracted from both the raw and preprocessed signals to form an original feature set. Second, a coupled feature selection scheme combining the distance evaluation technique and max-relevance and min-redundancy is carried out to obtain an optimal feature set. Finally, the deep belief network, a novel intelligent diagnosis method with a deep architecture, is applied to identify different gearbox health conditions. As the multi-sensor data fusion technique is utilized to provide sufficient and complementary information for fault diagnosis, this method holds the potential to overcome the shortcomings from an individual sensor that may not accurately describe the health conditions of gearboxes. Ten different gearbox health conditions are simulated to validate the performance of the proposed method. The results confirm the superiority of the proposed method in gearbox fault diagnosis.

scholarly journals Fault Diagnosis Based on Multi-sensor Data Fusion for Numerical Control Machine

2016 ◽  
Vol 12 (02) ◽  
pp. 29
Author(s):  
Yan Wen ◽  
Ji-wen Tan ◽  
Hong Zhan ◽  
Xian-bin Sun
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Machine Tool ◽  
Frequency Domain ◽  
Numerical Control ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multiple Classifiers ◽  
Multi Sensor Data Fusion ◽  
Control Machine

Fault diagnosis for numerical control machine is more difficult than that for other mechanical equipments due to its structural complexity and the coupling feature among different faults. In order to improve the accuracy and reliability of fault diagnosis for numerical control machine, an intelligent fault diagnosis model is studied. Besides the traditional method that multiple sensors are mounted on different locations, internal operation parameters from machine tool itself or NC program are introduced into the condition monitoring system because numerical machine tool is equipped with different kinds of sensors. These two information sources establish the multi-dimensional information system which provides the original information for diagnosis. On this base, the method based on multi-sensor data fusion is developed in this paper. Multiple characteristic parameters in time domain, frequency domain and time-frequency domain are extracted from the processed signal to mine the fault information. The sensitive parameter set which is regarded as the input characteristic vectors of classifiers is obtained on the base of correlation analysis. Multiple classifiers are enabled respectively and simultaneously to fuse all the sensitive parameters quantitatively and diagnose the fault type. Finally the results of multiple classifiers are fused in the form of global decision fusion by the method of fuzzy comprehensive evaluation to obtain the final diagnosis result. The determination method of weight based on classifier output's entropy is discussed in this paper and the formula is given. This model and method has been tested in rolling bearing fault diagnosis for numerical control machine and the results of the proposed model show which is effective and versatile.

Fault Diagnosis of Induction Motor Based on Multi-Sensor Data Fusion

2014 ◽  
Vol 651-653 ◽  
pp. 729-732
Author(s):  
Shu Ying Li ◽  
Mu Qin Tian ◽  
Lei Xue
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Induction Motor ◽  
Sensor Data ◽  
Fusion Technology ◽  
Accurate Identification ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Early Failures ◽  
Feature Information

For the conclusions of single parameter fault feature diagnosis has some uncertainty, in induction motor early fault, we proposed the use of multi-sensor data fusion technology, acted signal processing to the collected current, vibration and temperature, extracted feature information failure, fused the evidence independent with each other using D-S evidence fusion rules. According to the final combination results of all the evidence, combined with intermediate results of the evidence combination, we achieved the accurate identification of induction motor rotor early failures and composite fault. The diagnosis examples show that the use of multi-sensor data fusion technology can significantly improve the accuracy and reliability of early fault diagnosis.

Multi-Sensor Data Fusion Technology Based on BP Neural Network Application in the Coal Mine Equipment Fault Diagnosis

2014 ◽  
Vol 678 ◽  
pp. 238-241 ◽  
Author(s):  
Xiang Zhong Meng ◽  
Hui Long Liu ◽  
Zi Sheng Hou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Bp Neural Network ◽  
Sensor Data ◽  
Neural Network Learning ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Network Application ◽  
Fault State

In this paper, for the frequent faults problems of the mine air compressor main motor, we use the BP neural network learning algorithms on the basis of the theory of multi-sensor data fusion. The collected characteristic signals were processed by the method of data fusion, and we could get the current motor fault state value. Compared to the experimental results, it can realize the fault diagnosis of mine equipment obviously.

Multi-Sensor Data Fusion for Rotating Machinery Fault Diagnosis Using Residual Convolutional Neural Network

2021 ◽  
Author(s):  
Tingli Xie ◽  
Xufeng Huang ◽  
Seung-Kyum Choi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Convolutional Neural Network ◽  
Manufacturing Systems ◽  
Computational Cost ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method

Abstract Diagnosis of mechanical faults in the manufacturing systems is critical for ensuring safety and saving cost. With the development of data transmission and sensor technologies, the measuring systems can easily acquire multi-sensor and massive data. The traditional fault diagnosis methods usually depend on the features extracted by experts manually. The feature extraction process is usually time-consuming and laborious, which has a significant impact on the final results. Although Deep-Learning (DL) provides an end-to-end way to address the drawbacks of traditional methods, it is necessary to do deep research on an intelligent fault diagnosis method based on Multi-Sensor Data and Data Fusion. In this project, a novel intelligent diagnosis method based on Multi-Sensor Data Fusion and Convolutional Neural Network (CNN) is explored, which can automatically extract features from raw signals and achieve superior recognition performance. Firstly, a Multi-Signals-to-RGB-Image conversion method based on Principal Component Analysis (PCA) is applied to fuse multi-signal data into three-channel RGB images, which can eliminate the effect of handcrafted features and obtain the feature-level fused information. Then, the improved CNN with residual networks and the Leaky Rectified Linear Unit (LReLU) is defined and trained by the training samples, which can balance the relationship between computational cost and accuracy. After that, the testing data are fed into CNN to obtain the final diagnosis results. Two datasets, including the KAT bearing dataset and Gearbox dataset, are conducted to verify the effectiveness of the proposed method. The comprehensive comparison and analysis with widely used algorithms are also performed. The results demonstrate that the proposed method can detect different fault types and outperform other methods in terms of classification accuracy. For the KAT bearing dataset and Gearbox dataset, the proposed method’s average prediction accuracy is as high as 99.99% and 99.98%, which demonstrates that the proposed method achieves more reliable results than other DL-based methods.

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