Classification Of Fault Signals Research Articles

Multi-resolution short-time Fourier transform providing deep features for 3D CNN to classify rolling bearing fault vibration signals

The time-frequency domain features of vibration signals provide valuable information for deep learning-based rolling bearing fault diagnosis methods, where fault signal classification aiding in the identification of nominal fault types during diagnosis. The Short-Time Fourier Transform (STFT) is a widely used time-frequency transformation method, and its window length is the key parameter that determines the trade-off between time and frequency resolution. The primary motivation of this study is to address the limitation in traditional STFT-based 2D CNN methods: the inability to adapt the window length to different types of signals. To achieve accurate classification of bearing fault types, this study proposes a method based on three-dimensional convolutional neural networks (3D CNNs) to deeply explore the time-frequency domain information of one-dimensional vibration signals from faulty bearings. This method first applies STFT with multiple window sizes to perform multi-resolution time-frequency transformations on the time-domain vibration signals, yielding three-dimensional data. Subsequently, a classifier is trained based on the proposed 3D CNN. Experimental results on public datasets show that, without any sophisticated techniques, the proposed method achieves an average classification accuracy of 99.2% for six types of bearing faults using a relatively simple CNN structure. Compared to 1D CNN and 2D CNN methods that use fixed window sizes for STFT, the proposed method significantly enhances classification performance. Furthermore, it demonstrates robust classification results even on small-scaled bearing datasets.

A novel enhanced deep learning-based fault diagnosis approach for cascaded multilevel inverter

With the widespread use of power electronic converters like Multilevel Inverters (MLI) in various applications, several challenges arise due to increased switch count, capacitor surge current, and reverse current from inductive loads. These factors lead to the onset of faults, which negatively affect the reliability of MLI. Hence, an efficient fault detection and fault tolerant technique are necessary to ensure the reliability of the inverter. The deep learning-based fault detection approach is proposed for a 15-level Cascaded H-Bridge (CHB) MLI to address this issue. The proposed technique involves pre-processing with an Adaptive Hilbert-Huang filter, segmentation using Partial Differential Equation (PDE), feature extraction using Scale-Invariant Feature Transform (SIFT), and classification of fault signals with a Crow Swarm Algorithm (CSA) optimized Convolution Neural Network (CNN) classifier. To evaluate the effectiveness of the proposed approach in fault classification, four complex fault types were considered using MATLAB. The results show that CSA-optimized CNN offers a remarkable classification accuracy of 99.84%.

A sample entropy inspired affinity propagation method for bearing fault signal classification

For bearing fault signal classification, the performance of the affinity propagation (AP) method is limited by its way of measuring similarity. In this paper, we have proposed an improved version for the AP method, which is termed as IMFSE-AP. The key point of the proposed IMFSE-AP method is the improvement on the way of measuring signal similarity. Instead of the Euclidean distance, a novel method based on the sample entropy of IMFs (IMFSE) is proposed, which can measure signal similarities better with respect to data complexity. In the proposed similarity measurement method, signals are first decomposed into IMFs by the EEMD method. Then the distances of sample entropy vectors of IMFs are computed to evaluate similarities between signals. Numerical experiments conducted on synthetic signals and real bearing fault signals illustrate the good performance of the proposed method.

A New Signal Classification Method Based on EEMD and FCM and its Application in Bearing Fault Diagnosis

In order to diagnose nonlinear and non-stationary fault signals in bearings, a new method is presented based on the ensemble empirical decomposition (EEMD) and the fuzzy c-means (FCM) clustering algorithm. At first, the bearing fault signals were decomposed using EEMD and the intrinsic mode functions (IMF) were produced. Second the energy ratios of these IMFs were computed and taken as the characteristic parameters for the FCM clustering algorithm. Then the FCM clustering method was conducted to classify the bearing fault signals into different classes. Finally, on the basis of the preceding classification results, we diagnosed a bearing fault through taking its distances between different cluster centers as the criteria. Experiments showed that the bearing fault signal classification results conformed to actualities well. The new signal classification method can be effectively utilized in bearing fault diagnosis.

Fault Signal Classification using Adaptive Boosting Algorithm

In recent years, researchers seldom investigate how to boost the classification performance of any learning algorithm for fault signal detection. We propose a fault signal classification method based on adaptive boosting (adaboost) in this paper. Adaboost is able to select an optimal linear combination of classifiers to form an ensemble whose joint decision rule has relatively high accuracy on the training set. First, we extract statistical features from sample signals. And then we make use of a decision tree to identify optimal features, which are used to classify the sample set by adaboost algorithm. To verify its accuracy, we set up the roller bearing experiment. Practical results show that the method can precisely identify fault signals, and be comparable to SVM based traditional method.DOI: http://dx.doi.org/10.5755/j01.eee.18.8.2635

Multi-sensor data fusion using support vector machine for motor fault detection

Motor fault diagnosis in dynamic condition is a typical multi-sensor data fusion problem. It involves the use of information collected from multiple sensors, such as vibration, sound, current, voltage, and temperature, to detect and identify motor faults. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, the multi-sensor based motor fault diagnosis can be viewed as the problem of evidence fusion. In this article we propose and investigate a hybrid method for fault signal classification based on sensor data fusion by using the Support Vector Machine (SVM) and Short Term Fourier Transform (STFT) techniques. We report a practical application of this hybrid model and evaluate its performance. Finally, we compare the performance of the proposed system against some other standard fault classification techniques.