Fault diagnosis of rolling bearing with incomplete labels using weakly labeled support vector machine

The fault diagnosis of rolling bearing has attracted increasing attention in recent years on account of the significant impact on the functionality and efficiency of complex primary system. In consideration of the bearing samples with incomplete labels, this paper investigates the possibilities of a novel fault diagnosis method using the experience of image cognition theory in dealing with the fault state classification of rolling bearings, aiming to realize fault classification that only utilizes a small amount of labeled bearing data. In this paper empirical mode decomposition (EMD) is firstly applied to the original signal, where the basic time domain features are extracted from the first three intrinsic mode functions (IMFs), and are set as the inputs of the following classifier for final training and testing. Weakly labeled support vector machine (WELLSVM), which seems more efficient than inductive support vector machines especially in the case of very small training sets and large test sets, is then established via a novel label generation strategy in the method of semi-supervised learning. Validation data are collected to facilitate the comparison and evaluation of the fault diagnosis results, of which the labeled data proportion is diverse from each other. The results indicates the effectiveness of the proposed method for bearing fault diagnosis with weakly labeled data

Classification Of Rotating Machinery Fault Using Vibration Signal

Rotating machinery are critical instruments in the manufacturing sectors that are continually operated to fulfill their productivity objective. To reduce the risk of catastrophic failure and unwanted breakdown, it is crucial to ensure that these machines operate within their quality standards. Waste is undesirable to such sectors that directly affect manufacturing price. Maintenance intervention must be efficient, else it is deemed as waste. It is estimated that businesses are losing billions of dollars worldwide due to inadequate maintenance and poor management. It is, therefore, crucial to carry out effective maintenance actions. Since condition-based monitoring method recommends maintenance only when necessary, this approach can avoid unnecessary plan maintenance costs. Condition-based approach, along with the different faults detecting and correcting approach can become handy for the smooth operation of the machine in the industries. Out of various approaches, the vibration parameters-based condition monitoring approach has been proposed in this work. The significance of the proposed method is that it can correctly identify and classify the condition of the equipment as normal, misaligned, unbalanced, and cracked. Using the information of local harmonic acceleration amplitude, instead of harmonic acceleration amplitude, fault detecting, and classifying method is proposed. Then, the phase plane diagram-based fault classification technique is also proposed using the information of all the accelerometer data. Similarly, the Fuzzy Logic method is also used for fault detection and classification purpose. The obtained results signify the effectiveness of these proposed methods

Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions.

Incipient fault diagnosis of a bearing requires robust feature representation for an accurate condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to manual features and traditional machine learning approaches such as artificial neural networks (ANN) and support vector machines (SVM). These handcrafted features require substantial human expertise and domain knowledge. In addition, these feature characteristics vary with the bearing's rotational speed. Thus, such methods do not yield the best results under variable speed conditions. To address this issue, this paper presents a reliable fault diagnosis scheme based on acoustic spectral imaging (ASI) of acoustic emission (AE) signals as a precise health state. These health states are further utilized with transfer learning, which is a machine learning technique, which shares knowledge with convolutional neural networks (CNN) for accurate diagnosis under variable operating conditions. In ASI, the amplitudes of the spectral components of the windowed time-domain acoustic emission signal are transformed into spectrum imaging. ASI provides a visual representation of acoustic emission spectral features in images. This ensures enhanced spectral images for transfer learning (TL) testing and training, and thus provides a robust classifier technique with high diagnostic accuracy

Roller element bearing acoustic fault detection using smartphone and consumer microphones

Roller element bearings are a common component and crucial to most rotating machinery; their failure makes up around half of the total machine failures, each with the potential to cause extreme damage, injury and downtime. Fault detection through condition monitoring is of significant importance. This paper demonstrates bearing fault detection using widely accessible consumer audio tools. Audio measurements from a smartphone and a standard USB microphone, and vibration measurements from an accelerometer are collected during tests on an electrical induction machine exhibiting a variety of mechanical bearing anomalies. A peak finding method along with use of trained Support Vector Machines (SVMs) classify the faults. It is shown that the classification rate from both the smartphone and the USB microphone was 95 and 100%, respectively, with the direct physically detected vibration results achieving only 75% classification accuracy. This work opens up the opportunity of using readily affordable and accessible acoustic diagnosis and prognosis for early mechanical anomalies on rotating machines

Comparison between Artificial Neural Network and Support Vector Method for a Fault Diagnostics in Rolling Element Bearings

AbstractRolling element bearings are the most crucial part of any rotating machines. The failures of bearing without warning will result catastrophic consequences in many situations. Therefore condition monitoring of bearing is very important. In this paper, artificial intelligence techniques are used to predict and analyses the bearing faults. Experiments were carried out on rolling bearing having localized defects on the various bearing components for wide range of speed and vibration signals were stored. Condition monitoring systems is divided in two important part one feature extraction and second diagnosis through extracted features. Daubechies wavelet is popular for smoothing of signals so, it is chosen for reducing the background noise from vibration signal. Kurtosis, RMS, Creast factor and Peak difference as suitable time domains features are extracted from decompose time velocity signals. Back propagation multilayer neural network was train and tested by 369 pre-treated normliesed features. Support vector machine is also used for the same data for predicting bearing faults. Finally, it is found that Support vector machine techniques gives better results over ANN

Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations.

International audienceIn this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD).Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive resonance theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN-SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD-LDA-PNN-SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it’s validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets

Bearing fault diagnosis via kernel matrix construction based support vector machine

A novel approach on kernel matrix construction for support vector machine (SVM) is proposed to detect rolling element bearing fault efficiently. First, multi-scale coefficient matrix is achieved by processing vibration sample signal with continuous wavelet transform (CWT). Next, singular value decomposition (SVD) is applied to calculate eigenvector from wavelet coefficient matrix as sample signal feature vector. Two kernel matrices i.e. training kernel and predicting kernel, are then constructed in a novel way, which can reveal intrinsic similarity among samples and make it feasible to solve nonlinear classification problems in a high dimensional feature space. To validate its diagnosis performance, kernel matrix construction based SVM (KMCSVM) classifier is compared with three SVM classifiers i.e. classification tree kernel based SVM (CTKSVM), linear kernel based SVM (L-SVM) and radial basis function based SVM (RBFSVM), to identify different locations and severities of bearing fault. The experimental results indicate that KMCSVM has better classification capability than other methods

Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model

Rolling bearings are one of the most widely used bearings in industrial machines. Deterioration in the condition of rolling bearings can result in the total failure of rotating machinery. AI-based methods are widely applied in the diagnosis of rolling bearings. Hybrid NN-based methods have been shown to achieve the best diagnosis results. Typically, raw data is generated from accelerometers mounted on the machine housing. However, the diagnostic utility of each signal is highly dependent on the location of the corresponding accelerometer. This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a shaft-mounted wireless acceleration sensor. The experimental results show that the hybrid model is superior to the CNN and MLP models operating separately, and can deliver a high detection accuracy of 99,6% for the bearing faults compared to 98% for CNN and 81% for MLP models