Sheraz Ali Khan

Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors

Optimal feature distribution and feature selection are of paramount importance for reliable fault diagnosis in induction motors. This paper proposes a hybrid feature selection model with a novel discriminant feature distribution analysis-based feature evaluation method. The hybrid feature selection employs a genetic algorithm- (GA-) based filter analysis to select optimal features and a -NN average classification accuracy-based wrapper analysis approach that selects the most optimal features. The proposed feature selection model is applied through an offline process, where a high-dimensional hybrid feature vector is extracted from acquired acoustic emission (AE) signals, which represents a discriminative fault signature. The feature selection determines the optimal features for different types and sizes of single and combined bearing faults under different speed conditions. The effectiveness of the proposed feature selection scheme is verified through an online process that diagnoses faults in an unknown AE fault signal by extracting only the selected features and using the -NN classification algorithm to classify the fault condition manifested in the unknown signal. The classification performance of the proposed approach is compared with those of existing state-of-the-art average distance-based approaches. Our experimental results indicate that the proposed approach outperforms the existing methods with regard to classification accuracy.

Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing’s speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds.

Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines

This letter presents a multi-fault diagnosis scheme for bearings using hybrid features extracted from their acoustic emissions and a Bayesian inference-based one-against-all support vector machine (Bayesian OAASVM) for multi-class classification. The Bayesian OAASVM, which is a standard multi-class extension of the binary support vector machine, results in ambiguously labeled regions in the input space that degrade its classification performance. The proposed Bayesian OAASVM formulates the feature space as an appropriate Gaussian process prior, interprets the decision value of the Bayesian OAASVM as a maximum a posteriori evidence function, and uses Bayesian inference to label unknown samples.

Incipient fault diagnosis in bearings under variable speed conditions using multiresolution analysis and a weighted committee machine

Incipient defects in bearings are traditionally diagnosed either by developing discriminative models for features that are extracted from raw acoustic emission (AE) signals, or by detecting peaks at characteristic defect frequencies in the envelope power spectrum of the AE signals. Under variable speed conditions, however, such methods do not yield the best results. This letter proposes a technique for diagnosing incipient bearing defects under variable speed conditions, by extracting features from different sub-bands of the inherently non-stationary AE signal, and then classifying bearing defects using a weighted committee machine, which is an ensemble of support vector machines and artificial neural networks. The proposed method also improves the generalization performance of the neural networks to enhance their classification accuracy, particularly with limited training data.

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal. These defect frequencies depend upon the inherently nonstationary shaft speed. Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed, whereas design variations require retraining a new instance of the classifier for each operating speed. This paper presents an automated approach for fault diagnosis in bearings based upon the 2D analysis of vibration acceleration signals under variable speed conditions. Images created from the vibration signals exhibit unique textures for each fault, which show minimal variation with shaft speed. Microtexture analysis of these images is used to generate distinctive fault signatures for each fault type, which can be used to detect those faults at different speeds. A -nearest neighbor classifier trained using fault signatures generated for one operating speed is used to detect faults at all the other operating speeds. The proposed approach is tested on the bearing fault dataset of Case Western Reserve University, and the results are compared with those of a spectrum imaging-based approach.

Multi-fault Diagnosis of Roller Bearings Using Support Vector Machines with an Improved Decision Strategy

This paper proposes an efficient fault diagnosis methodology based on an improved one-against-all multiclass support vector machine (OAA-MCSVM) for diagnosing faults inherent in rotating machinery. The methodology employs time and frequency domain techniques to extract features of diverse bearing defects. In addition, the proposed method introduces a new reliability measure (SVMReM) for individual SVMs in the multiclass framework. The SVMReM achieves optimum results irrespective of the test sample location by using a new decision strategy for the proposed OAA-MCSVM based method. Finally, each SVM is trained with optimized kernel parameters using a grid search technique to enhance the classification accuracy of the proposed method. Experimental results show that the proposed method is superior to conventional approaches, yielding an average classification accuracy of 97 % for five different rotational speed conditions, eight different fault types and two different crack sizes.

Rotational speed invariant fault diagnosis in bearings using vibration signal imaging and local binary patterns

Structural vibrations of bearing housings are used for diagnosing fault conditions in bearings, primarily by searching for characteristic fault frequencies in the envelope power spectrum of the vibration signal. The fault frequencies depend on the non-stationary angular speed of the rotating shaft. This paper explores an imaging-based approach to achieve rotational speed independence. Cycle length segments of the rectified vibration signal are stacked to construct grayscale images which exhibit unique textures for each fault. These textures show insignificant variation with the rotational speed, which is confirmed by the classification results using their local binary pattern histograms.

Protocols and Mechanisms to Recover Failed Packets in Wireless Networks: History and Evolution

The emergence of multihop wireless networks and the increase in low-latency demands of error tolerant applications, such as voice over internet protocol, have triggered the development of new protocols and mechanisms for recovering failed packets. For example, recovering partially corrupt packets instead of retransmission has emerged as an effective way to improve key network performance metrics, such as goodput, latency, and energy consumption. A number of similar and interesting solutions have been proposed recently to either reconstruct or process corrupt packets on wireless networks. The proliferation of multimedia services on 3G and long term evolution networks, and the stringent quality of service requirements for these applications have given birth to robust codes and new error tolerant mechanisms for packet delivery. Despite years of active research in the field, we lack a comprehensive survey that summarizes recent developments in this area and highlights avenues with potential for future growth. This survey tries to fill in this void by providing a comprehensive review of the evolution of this field and underscoring areas for future research.

A Hybrid Prognostics Technique for Rolling Element Bearings Using Adaptive Predictive Models

Rolling element bearings cause the largest number of failures in induction motors. Predicting an impending failure and estimating the remaining useful life (RUL) of a bearing is essential for scheduling maintenance and avoiding abrupt shutdowns of critical systems. This paper presents a hybrid technique for bearing prognostics that utilizes regression-based adaptive predictive models to learn the evolving trend in a bearings health indicator. These models are then used to project forward in time and estimate the RUL of a bearing. The proposed algorithm addresses some key issues in existing methods for bearing health prognosis that affect their prognostic performance, specifically determining the time to start prediction (TSP), handling random fluctuations in a bearings health indicator, and setting a dynamic failure threshold. The proposed algorithm is validated on publicly available bearing prognostics data from the Center for Intelligent Maintenance Systems. Experimental results show that the proposed approach is effective in determining an accurate TSP and failure threshold, as well as handling random fluctuations. Moreover, this approach achieves excellent prognostic performance and estimates the RUL of bearings within the specified error bounds, even at points very close to the TSP, where traditional methods yield relatively poor RUL estimates.

An FPGA-Based Implementation of a Pipelined FFT Processor for High-Speed Signal Processing Applications

In this study, we propose an efficient, 1024 point, pipelined FFT processor based on the radix-2 decimation-in-frequency (R2DIF) algorithm using the single-path delay feedback (SDF) pipelined architecture. The proposed FFT processor is designed as an intellectual property (IP) logic core for easy integration into digital signal processing (DSP) systems. It employs the shift-add method to optimize the multiplication of twiddle factors instead of the dedicated, embedded functional blocks. The proposed design is implemented on a Xilinx Virtex-7 field programmable gate array (FPGA). The experimental results show that the proposed FFT design is more efficient in terms of speed, accuracy and resource utilization as compared to existing designs and hence more suitable for high-speed DSP applications.