Showing papers on "Bearing (mechanical) published in 2017"

Deep Learning Based Approach for Bearing Fault Diagnosis

Abstract: Bearing is one of the most critical components in most electrical and power drives. Effective bearing fault diagnosis is important for keeping the electrical and power drives safe and operating normally. In the age of Internet of Things and Industrial 4.0, massive real-time data are collected from bearing health monitoring systems. Mechanical big data have the characteristics of large volume, diversity, and high velocity. There are two major problems in using the existing methods for bearing fault diagnosis with big data. The features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise, and the used models have shallow architectures, limiting their capability in fault diagnosis. Effectively mining features from big data and accurately identifying the bearing health conditions with new advanced methods have become new issues. This paper presents a deep learning-based approach for bearing fault diagnosis. The presented approach preprocesses sensor signals using short-time Fourier transform (STFT). Based on a simple spectrum matrix obtained by STFT, an optimized deep learning structure, large memory storage retrieval (LAMSTAR) neural network, is built to diagnose the bearing faults. Acoustic emission signals acquired from a bearing test rig are used to validate the presented method. The validation results show the accurate classification performance on various bearing faults under different working conditions. The performance of the presented method is also compared with other effective bearing fault diagnosis methods reported in the literature. The comparison results have shown that the presented method gives much better diagnostic performance, even at relatively low rotating speeds.

Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests

Abstract: The faults of rolling bearings can result in the deterioration of rotating machine operating conditions, how to extract the fault feature parameters and identify the fault of the rolling bearing has become a key issue for ensuring the safe operation of modern rotating machineries. This paper proposes a novel hybrid approach of a random forests classifier for the fault diagnosis in rolling bearings. The fault feature parameters are extracted by applying the wavelet packet decomposition, and the best set of mother wavelets for the signal pre-processing is identified by the values of signal-to-noise ratio and mean square error. Then, the mutual dimensionless index is first used as the input feature for the classification problem. In this way, the best features of the five mutual dimensionless indices for the fault diagnosis are selected through the internal voting of the random forests classifier. The approach is tested on simulation and practical bearing vibration signals by considering several fault classes. The comparative experiment results show that the proposed method reached 88.23% in classification accuracy, and high efficiency and robustness in the models.

Translational and Scaling Formation Maneuver Control via a Bearing-Based Approach

Abstract: This paper studies distributed maneuver control of multiagent formations in arbitrary dimensions. The objective is to control the translation and scale of the formation while maintaining the desired formation pattern. Unlike conventional approaches where the target formation is defined by relative positions or distances, we propose a novel bearing-based approach where the target formation is defined by inter-neighbor bearings. Since the bearings are invariant to the translation and scale of the formation, the bearing-based approach provides a simple solution to the problem of translational and scaling formation maneuver control. Linear formation control laws for double-integrator dynamics are proposed and the global formation stability is analyzed. This paper also studies bearing-based formation control in the presence of practical problems, including input disturbances, acceleration saturation, and collision avoidance. The theoretical results are illustrated with numerical simulations.

A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection

Abstract: Bearings faults are one of the main causes of breakdown of rotating machines Thus detection and diagnosis of mechanical faults in bearings is very crucial for the reliable operation A novel intelligent fault diagnosis method for roller bearings based on affinity propagation (AP) clustering algorithm and adaptive feature selection technique is proposed to better equip with a non-expert to carry out diagnosis operations Ensemble empirical mode decomposition (EEMD) and wavelet packet transform (WPT) are utilized to accurately extract the fault characteristic information buried in the vibration signals Moreover, in order to improve the efficiency of clustering algorithm and avoid the curse of dimensionality, a new adaptive features selection technique is developed in this work, whose effectiveness is verified in comparison with other methods The proposed intelligent method is then applied to the bearing fault diagnosis Results demonstrate that the proposed method is able to reliably and accurately identify different fault categories and severities of bearings

Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

Abstract: Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.

Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures

Abstract: Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e., normal bearing and three different types of defected bearings on outer race, inner race, and roller separately. Particle swarm optimization and Broyden-Fletche—Goldfarb-Shanno-based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modeling-based continuous wavelet transform model. Then, a 3-D feature space of the statistical parameters and a nearest neighbor classifier are, respectively, applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment.

An adaptive deep convolutional neural network for rolling bearing fault diagnosis

Abstract: The working condition of rolling bearing usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of the dependence on manual feature design, the deep CNN model is initialized. Secondly, to adapt to different signal characteristics, the main parameters of deep CNN model are determined with particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to analyze the rolling bearing vibration signals collected from an experimental setup and electrical locomotive, and the analysis results confirm that the proposed method has more accurate and robust performance than other intelligent methods.

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Abstract: Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Abstract: Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.

Detection of Bearing Faults in Mechanical Systems Using Stator Current Monitoring

Abstract: Induction motors have been responsible for running mechanical systems in the industry for many decades. Their diagnosis still remains a hot quest for the researchers using various techniques. In this study, motor current signature analysis (MCSA) technique has been used to detect the faulty bearing installed in load machine (coupled to an induction motor). It has been seen that faulty bearings installed in load machines do not directly alter airgap eccentricity of an induction motor. In fact, these bearing faults affect the resultant torque of an induction motor. As modulating fault components show very low amplitude, these are usually masked by noise. This paper is devoted towards extracting features of faulty components efficiently from stator current using continuous wavelet transform. This methodology is assessed for detecting outer race faults in bearings installed in load machines using MCSA.

Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study:

Abstract: Intelligent fault-diagnosis methods using machine learning techniques like support vector machines and artificial neural networks have been widely used to distinguish bearings’ health condition. Ho...

Online Fault Diagnosis of Motor Bearing via Stochastic-Resonance-Based Adaptive Filter in an Embedded System

Abstract: Digital signal processing algorithms are widely adopted in motor bearing fault diagnosis. However, most algorithms are developed on desktop platforms, and their focus is on the analysis of offline captured signals. In this paper, a simple and easily implemented algorithm running on an embedded system is proposed for the online fault diagnosis of motor bearing. The core part of the algorithm is a stochastic-resonance-based adaptive filter that realizes signal denoising and adaptation of the filter coefficient. Processed by the filter, the period of the purified signal is obtained, and then the fault type of the motor bearing is identified. The proposed method has distinct merits, such as low computational cost, online implementation, contactless measurement, and availability for various speed motors. This paper provides a simple, flexible, and effective solution for conducting motor bearing diagnosis on an embedded/portable device. The algorithm proposed is validated by a brushless dc motor and a brushed dc motor fabricating with defective/healthy support bearings.