An improved spline-local mean decomposition and its application to vibration analysis of rotating machinery with rub-impact fault

A troublesome problem in application of local mean decomposition (LMD) is that the moving averaging process is time-consuming and inaccurate in processing the mechanical vibration signals. An improved spline-LMD (SLMD) method is proposed to solve this problem. The proposed method uses the cubic spline interpolation to compute the upper and lower envelopes of a signal, and then the local mean and envelope estimate functions can be derived using the envelopes. Meanwhile, a signal extending approach based on self-adaptive waveform matching technique is applied to extend the raw signal and overcome the boundary distortion resulting from the process of computing the upper and lower envelopes. Subsequently, this paper compares SLMD with LMD in four aspects through a simulative signal. The comparative results illustrate that SLMD consumes less computation time and produces more accurate decomposed results than LMD. In the experimental part, SLMD and LMD are respectively applied to analyze the vibration signals resulting from a rotor-bearing system with rub-impact fault. The results show that SLMD can more efficiently and accurately extract the important fault features, which demonstrates that SLMD performs better than LMD in analyzing the mechanical vibration signals

Applications on Ultrasonic Wave

This book presents applications on the ultrasonic wave for material characterization and nondestructive evaluations. It could be of interest to the researchers and students who are studying on the fields of ultrasonic waves

Application of variational mode decomposition in vibration analysis of machine components

Monitoring and diagnosis of machinery in maintenance are often undertaken using vibration analysis. The machine vibration signal is invariably complex and diverse, and thus useful information and features are difficult to extract. Variational mode decomposition (VMD) is a recent signal processing method that able to extract some of important features from machine vibration signal. The performance of the VMD method depends on the selection of its input parameters, especially the mode number and balancing parameter (also known as quadratic penalty term). However, the current VMD method is still using a manual effort to extract the input parameters where it subjects to interpretation of experienced experts. Hence, machine diagnosis becomes time consuming and prone to error. The aim of this research was to propose an automated parameter selection method for selecting the VMD input parameters. The proposed method consisted of two-stage selections where the first stage selection was used to select the initial mode number and the second stage selection was used to select the optimized mode number and balancing parameter. A new machine diagnosis approach was developed, named as VMD Differential Evolution Algorithm (VMDEA)-Extreme Learning Machine (ELM). Vibration signal datasets were then reconstructed using VMDEA and the multi-domain features consisted of time-domain, frequency-domain and multi-scale fuzzy entropy were extracted. It was demonstrated that the VMDEA method was able to reduce the computational time about 14% to 53% as compared to VMD-Genetic Algorithm (GA), VMD-Particle Swarm Optimization (PSO) and VMD-Differential Evolution (DE) approaches for bearing, shaft and gear. It also exhibited a better convergence with about two to nine less iterations as compared to VMD-GA, VMD-PSO and VMD-DE for bearing, shaft and gear. The VMDEA-ELM was able to illustrate higher classification accuracy about 11% to 20% than Empirical Mode Decomposition (EMD)-ELM, Ensemble EMD (EEMD)-ELM and Complimentary EEMD (CEEMD)-ELM for bearing shaft and gear. The bearing datasets from Case Western Reserve University were tested with VMDEA-ELM model and compared with Support Vector Machine (SVM)-Dempster-Shafer (DS), EEMD Optimal Mode Multi-scale Fuzzy Entropy Fault Diagnosis (EOMSMFD), Wavelet Packet Transform (WPT)-Local Characteristic-scale Decomposition (LCD)- ELM, and Arctangent S-shaped PSO least square support vector machine (ATSWPLM) models in term of its classification accuracy. The VMDEA-ELM model demonstrates better diagnosis accuracy with small differences between 2% to 4% as compared to EOMSMFD and WPT-LCD-ELM but less diagnosis accuracy in the range of 4% to 5% as compared to SVM-DS and ATSWPLM. The diagnosis approach VMDEA-ELM was also able to provide faster classification performance about 6 40 times faster than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). This study provides an improved solution in determining an optimized VMD parameters by using VMDEA. It also demonstrates a more accurate and effective diagnostic approach for machine maintenance using VMDEA-ELM

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Advanced control system for stand-alone diesel engine driven-permanent magnetic generator sets

The main focus is on the development of an advanced control system for variable speed standalone diesel engine driven generator systems. An extensive literature survey reviews the historical development and previous relevant research work in the fields of diesel engines, electrical machines, power electronic converters, power and electronic systems. Models are developed for each subsystem from mathematical derivations with necessary simplifications made to reduce complexity while retaining the required accuracy. Initially system performance is investigated using simulation models in Matlab/Simulink. The AC/DC/AC power electronic conversion system used employs a voltage controlled dc link. The ac voltage is maintained at constant magnitude and frequency by using a dc-dc converter and a fixed modulation ratio VSI PWM inverter. The DC chopper provides fast control of the output voltage by dealing efficiently with transient conditions. A Variable Speed Fuzzy Logic Core (VSFLC) controller is combined with a classical control method to produce a novel hybrid controller. This provides an innovative variable speed control that responds to both load and speed changes. A new power balance based control strategy is proposed and implemented in the speed controller. Subsequently a novel overall control strategy is proposed to co-ordinate the hybrid variable speed controller and chopper controller to provide overall control for both fast and slow variations of system operating conditions. The control system is developed and implemented in hardware using Xilinx Foundation Express. The VHDL code for the complete control system design is developed and the designs are synthesised and analysed within the Xilinx environment. The controllers are implemented with XC95108-PC84 and XC4010-PC84 to provide a compact and cheap control system. A prototype experimental system is described and test results are obtained that show the combined control strategy to be very effective. The research work makes contributions in the areas of automatic control systems for diesel engine generator sets and CPLD/FPGA application that will benefit manufacturers and consumers.EPSR

Some aspects of high-torque, low-speed, brushless electric motors

Imperial Users onl

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Compressive Sampling and Feature Ranking Framework for Bearing Fault Classification with Vibration Signals

Failures of rolling element bearings are amongst the main causes of machines breakdowns. To prevent such breakdowns, bearing health monitoring is performed by collecting data from rotating machines, extracting features from the collected data, and applying a classifier to classify faults. To avoid the burden of much storage requirements and processing time of a tremendously large amount of vibration data, the present paper proposes a combined Compressive Sampling (CS) based on Multiple Measurement Vector (MMV) and Feature Ranking (FR) framework to learn optimally fewer features from a large amount of vibration data from which bearing health conditions can be classified. The CS-based on MMV model is the first step in this framework and provides compressively-sampled signals based on compressed sampling rates. In the second step, the search for the most important features of these compressively-sampled signals is performed using feature ranking and selection techniques. For that purpose, we have investigated the following: (1) two compressible representations of vibration signals that can be used within CS framework, namely, Fast Fourier Transform (FFT) based coefficients and thresholded Wavelet Transform (WT) based coefficients, and (2) several feature ranking and selection techniques, namely, three similarity-based techniques, Fisher Score (FS), Laplacian Score (LS), Relief-F; one correlation-based technique, Pearson Correlation Coefficients (PCC); and one independence test technique, Chi-Square (Chi-2) to select fewer features that can sufficiently represent the original vibration signals. These selected features, in combination with three of the popular classifiers - multinomial Logistic Regression classifier (LRC), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs), have been evaluated for the classification of bearing faults. Results show that the proposed framework achieves high classification accuracies with a limited amount of data using various combinations of methods, which outperform recently published results