Improving the weak feature extraction by adaptive stochastic resonance in cascaded piecewise-linear system and its application in bearing fault detection

In mechanical engineering field, early fault features are extremely weak and submerged in heavy noise, and the weak feature extraction is quite challenging. In this work, we apply the adaptive stochastic resonance in cascaded piecewise-linear system to extract the weak features. The adaptive stochastic resonance is realized by the quantum particle swarm algorithm. By optimizing system parameters, the efficiency of the feature extraction is improved greatly. As a result, the weak features can be easily extracted eventually. The effectiveness and the high-performance of the proposed method are verified by the numerical simulation and experimental data of rolling element bearings. The bearing fault under different motor loads is detected effectively, consequently confirming the robustness of the proposed method

Reliability sequential compliance method for a partially observable gear system subject to vibration monitoring

Assumptions accompanying exponential failure models are often not met in the standard sequential probability ratio test (SPRT) of many products. However, for most of the mechanical products, Weibull distribution conforms to their life distributions better compared to other techniques. The SPRT method for Weibull life distribution is derived in this paper, which enables the implementation of reliability compliance tests for gearboxes. Using historical failure data and condition monitoring data, a life prediction model based on hidden Markov model (HMM) is established to describe the deterioration process of gearboxes, then the predicted remaining useful life (RUL) is transformed into failure data that is used in SPRT for further analysis, which can significantly save on testing time and reduce costs. Explicit expression for the distribution of RUL is derived in terms of the posterior probability that the system is in the unhealthy state. The predicted and actual values of the residual life are compared, and the average relative error is 3.90 %, which verifies the validity of the proposed residual life prediction approach. A comparison with other life prediction and SPRT methods is given to elucidate the efficacy of the proposed approach

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

Detection of a transverse crack in a nonlinear rotor using non-stationary response

To accurately detect an early transverse crack in the rotating machinery caused by fatigue or creep, various kinds of diagnosis systems utilizing the steady-state responses of a cracked rotor were developed. The paper focuses on the non-stationary response of a rotor system such as the startup, the shutdown or the variable running speeds of a rotating machinery to detect a transverse crack and to overcome the defects of the diagnosis system utilizing steady-state responses. Non-stationary characteristics during passages through the major resonance and various kinds of resonances are studied numerically and experimentally. The results demonstrate that the changes of vibration characteristics in a non-stationary response can detect a crack in a wide rational speed range and also prevent the dangerous operation for detecting a crack

Pronóstico de vida útil remanente en rodamientos con base en la estimación de la probabilidad de la degradación

La demanda de energía eléctrica por parte del sector industrial global solo crece, y se ve afectada fundamentalmente por el hecho que los rodamientos instalados en los motores eléctricos industriales se constituyen en el modo primario de fallo que afecta dicho consumo energético. Por tanto, la demanda de mantenimiento eficiente en motores eléctricos es crítica. Como solución, típicamente se ha empleado el mantenimiento preventivo como filosofía para la gestión de activos donde se busca maximizar la operación mediante inspecciones de rutina con mayor frecuencia cuando se exhiben anomalías, pero esto conlleva a un aumento en la probabilidad de falla debido a la intervención continua y el error humano inherente. Este documento de tesis doctoral presenta un marco integrado de diagnóstico y pronóstico para tratar con la vida útil remanente en rodamientos, con base en la estimación de la probabilidad de la degradación sujeta a modos de fallos definidos y severidades inducidas. Los enfoques metodológicos presentados incorporan análisis de vibración, para apoyar activamente el diagnóstico de fallos de forma no destructiva ni invasiva de máquinas rotativas en etapas tempranas, pero suponen un desafío con respecto a las propiedades de la señal, por ejemplo, su alto componente dinámico y de no estacionariedad. Se trabaja bajo una metodología que supone degradación de rodamientos evidenciada por una serie de estados discretos que representan efectivamente la dinámica y no estacionariedad del proceso de fallo. El conocimiento empírico previo también se incorpora dentro del sistema integrado para la clasificación de fallos y severidades. En definitiva, la metodología propuesta caracteriza diferentes firmas de falla en rodamientos empleando señales de vibración y varios dominios de representación de señales, con el propósito de tratar con la naturaleza estocástica y relaciones complejas en los datos concernientes a fallos y severidades. En la selección de características, se lleva a cabo un estudio sobre fusión y selección de dominios y características para la representación de señales, con el fin de discriminar la información relevante. Específicamente, aquí se presentan esquemas de fusión y selección basados en procedimientos de relevancia forward y backward, así como un enfoque estocástico de selección de características. Estas técnicas están destinadas a resaltar las características relevantes de múltiples dominios de las señales de vibración para las tareas de diagnóstico de fallos y evaluación de severidad, al mismo tiempo que se reduce la dimensionalidad de los datos. Para la etapa de entrenamiento, los enfoques se basan en sistemas estocásticos relacionados con la estimación de la probabilidad de un conjunto de estados discretos, tales como: Modelos Ocultos de Markov con observación discreta, Modelos Ocultos de Markov con observación continua y Modelos Ocultos de Markov Jerárquicos. El marco de diagnóstico y pronóstico integrado también se prueba como una herramienta de análisis de relevancia de características para discriminar múltiples condiciones de salud en rodamientos con caracterización multi-dominio. Los resultados logrados sobre una base de datos pública demuestran que los sistemas propuestos superan los algoritmos del estado-del-arte en cuanto a la cantidad de características seleccionadas y la eficiencia de la clasificación. Además, los resultados de las pruebas experimentales y los procedimientos de validación enseñan que el enfoque propuesto tiene la capacidad de proporcionar una advertencia de condiciones anormales del sistema mediante la identificación de las etapas tempranas de las condiciones de fallo. Las metodologías propuestas y técnicas analíticas desarrolladas en esta investigación para el pronóstico a largo plazo de la vida útil remanente se pueden aplicar en una gran variedad de contextos

Mathematical Modeling and Simulation in Mechanics and Dynamic Systems

The present book contains the 16 papers accepted and published in the Special Issue “Mathematical Modeling and Simulation in Mechanics and Dynamic Systems” of the MDPI “Mathematics” journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity

Model Referenced Condition Monitoring of High Performance CNC Machine Tools

Generally, machine tool monitoring is the prediction of the system’s health based on signal acquisition and processing and classification in order to identify the causes of the problem. The producers of machine tools need to pay more attention to their products life cycle because their customers increasingly focus on machine tool reliability and costs. The present study is concerned with the development of a condition monitoring system for high speed Computer Numerical Control (CNC) milling machine tools. A model is a simplification of a real machine to visualize the dynamics of a mechatronic system. This thesis applies recent modelling techniques to represent all parameters which affect the accuracy of a component produced automatically. The control can achieve an accuracy approaching the tolerance restrictions imposed by the machine tool axis repeatability and its operating environment. The motion control system of the CNC machine tool is described and the elements, which compose the axis drives including both the electrical components and the mechanical ones, are analysed and modelled. SIMULINK models have been developed to represent the majority of the dynamic behaviour of the feed drives from the actual CNC machine tool. Various values for the position controller and the load torque have been applied to the motor to show their behaviour. Development of a mechatronic hybrid model for five-axis CNC machine tool using Multi-Body-System (MBS) simulation approach is described. Analysis of CNC machine tool performance under non-cutting conditions is developed. ServoTrace data have been used to validate the Multi-body simulation of tool-to-workpiece position. This thesis aspects the application of state of art sensing methods in the field of condition monitoring of electromechanical systems. The ballscrew-with-nut is perhaps the most prevalent CNC machine subsystem and the condition of each element is crucial to the success of a machining operation. It’s essential to know of the health status of ballscrew, bearings and nut. Acoustic emission analysis of machines has been carried out to determine the deterioration of the ballscrew. Standard practices such as use of a Laser Interferometer have been used to determine the position of the machine tool. A novel machine feed drive condition monitoring system using acoustic emission (AE) signals has been proposed. The AE monitoring techniques investigated can be categorised into traditional AE parameters of energy, event duration and peak amplitude. These events are selected and normalised to estimate remaining life of the machine. This method is shown to be successfully applied for the ballscrew subsystem of an industrial high-speed milling machine. Finally, the successful outcome of the project will contribute to machine tool industry making possible manufacturing of more accurate products with lower costs in shorter time

Modeling and Simulation in Engineering

The general aim of this book is to present selected chapters of the following types: chapters with more focus on modeling with some necessary simulation details and chapters with less focus on modeling but with more simulation details. This book contains eleven chapters divided into two sections: Modeling in Continuum Mechanics and Modeling in Electronics and Engineering. We hope our book entitled "Modeling and Simulation in Engineering - Selected Problems" will serve as a useful reference to students, scientists, and engineers

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition