scholarly journals Performance Improvement of Classifier in Fault Diagnosis of Rotating Machines Using Sensor Fusion Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 1136-1141
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Sensor Fusion ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Vibration Signal ◽  
Fault Classification ◽  
Rotating Machines ◽  
Rotating Machine ◽  
Vibration Signal Analysis

The shaft, rotor, bearing and gear are the important elements of the rotating machines. Most of the problems in rotating machines are caused due to bearings and shaft. The failure of rotating machine causes production downtime and economic & safety issues. Vibration signal analysis is highly accepted technique in fault diagnosis of rotating machine. For automation of fault diagnosis, machine learning approach has been followed. Machine learning classifies fault based on variation in signatures pattern of the machine. But its effectiveness gets reduced when it is used for multi fault class problem. So in the present work, sound signals are also used along with vibration signals for applying sensor fusion techniques. In sensor fusion, signals from various sensors are fused in three levels such as data fusion, feature fusion and decision level fusion and the fused data sets are used for fault classification using machine learning algorithm. The performance of each technique is studied in detail and compared using classification accuracy. A new method is proposed by combination of fusion techniques to enhance the performance

scholarly journals Application of Feature Fusion Using Coaxial Vibration Signal for Diagnosis of Rolling Element Bearings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Jiao ◽  
Jianhai Yue ◽  
Di Pei ◽  
Zhunqing Hu
Keyword(s):  
Fault Diagnosis ◽  
Feature Fusion ◽  
Random Noise ◽  
Vibration Signal ◽  
Fault Classification ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Rolling Element ◽  
Vibration Signal Analysis

The research of rolling element bearings (REBs) fault diagnosis based on single sensor vibration signal analysis is very common. However, the information provided by an individual sensor is very limited, and the robustness of the system is poor. In this paper, a novel fault diagnosis method based on coaxial vibration signal feature fusion (CVSFF) is proposed to fully analyze the multisensor information of the system and build a more reliable diagnostic system. An ensemble empirical mode decomposition (EEMD) method is used to decompose the original vibration signal into a number of intrinsic mode functions (IMFs). Then the autocorrelation analysis is introduced to reduce the random noise remaining in IMFs. After that, the Rényi entropy is calculated as the feature of bearings. Finally, the features of coaxial vibration signal are fused by a multiple-kernel learning support vector machine (MKL-SVM) to classify bearing conditions. In order to verify the effectiveness of the CVSFF method in REB diagnosis, eight data sets from the Case Western Reserve University Bearing Data Center are selected. The fault classification results demonstrate that the proposed approach is a valuable tool for bearing faults detection, and the fused feature from coaxial sensors improves fault classification accuracy for REBs.

scholarly journals Feature selection tree for automated machinery fault diagnosis

2019 ◽  
Vol 255 ◽  
pp. 02004
Author(s):  
Hui Kar Hoou ◽  
Ooi Ching Sheng ◽  
Lim Meng Hee ◽  
Leong Mohd Salman
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Fault Classification ◽  
Machine Learning Algorithm ◽  
Statistical Features ◽  
Local Optima ◽  
Sensor Signals ◽  
Machinery Fault Diagnosis

Intelligent machinery fault diagnosis commonly utilises statistical features of sensor signals as the inputs for its machine learning algorithm. Due to the abundance of statistical features that can be extracted from raw signals and the accuracy of inserting all the available features into the machine learning algorithm for machinery fault classification, less accurate fault classification may inadvertently result due to overfitting issues. It is therefore only by selecting the most representative features that overfitting outcomes can be avoided and classification accuracy be improved. Currently, the genetic algorithm (GA) is regarded as the most commonly used and reliable feature selection tool for the improvement of accuracy for any machine learning algorithm. However, the greatest challenge for GA is that it may fall into a local optima and be computationally demanding. To overcome this limitation, a feature selection tree (FST) is here proposed. Numerous experimental dataset feature selections were executed using FST and GA; their performance is compared and discussed. Analysis showed that the proposed FST resulted in identical or superior optimal feature subsets when compared to the renowned GA method, but with a 20-time faster simulation period. The proposed FST is therefore more efficient in performing feature selection task than GA.

A sensor fusion based approach for bearing fault diagnosis of rotating machine

2021 ◽  
pp. 1748006X2110448
Author(s):  
Tauheed Mian ◽  
Anurag Choudhary ◽  
Shahab Fatima
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Feature Fusion ◽  
Essential Element ◽  
Complete Information ◽  
Vibration Sensor ◽  
High Dimensionality ◽  
Maintenance Cost ◽  
Support Vector ◽  
Rotating Machines

Fault diagnosis in rotating machines plays a vital role in various industries. Bearing is the essential element of rotating machines, and early fault detection can reduce the maintenance cost and enhance machine availability. In complex industrial machinery, a single sensor has a limitation to capture complete information about fault conditions. Hence, there is a need to involve multiple sensors to diagnose all possible fault conditions effectively. In such situations, an efficient fusion of information is required to develop a reliable fault diagnosis system. In this work, a feature fusion approach is implemented using two different sensors, that is, a contact type vibration sensor and a non-invasive thermal imaging camera. Hilbert transform is applied to decompose raw vibration and thermal image data, and subsequently, features are extracted and fused into a single feature vector. However, the features are fused in a concatenation manner, but this stage has high dimensionality. Neighborhood component analysis (NCA) is applied to reduce this high dimensionality of the feature vector, followed by a relief algorithm (RA) to compute the relevance level to find the optimal features. Finally, these optimal features are used as an input feature vector to the support vector machine (SVM) to classify the faults. The proposed approach resulted in considerably improved classification accuracy and detection quality than individual sensors. Also, the relevance of the proposed approach is proved by comparing its performance with other prevalent feature fusion techniques.

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