Author: Parno RaharjoPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
An Investigation of Surface Vibration, Airbourne Sound and Acoustic Emission Characteristics of a Journal Bearing for Early Fault Detection and Diagnosis
Author: Parno RaharjoProceedings of the 4th International Conference on Industrial Engineering
Author: Andrey A. RadionovPublisher: Springer
ISBN: 3319956302
Category : Technology & Engineering
Languages : en
Pages : 2490
Book Description
This book highlights recent findings in industrial, manufacturing and mechanical engineering, and provides an overview of the state of the art in these fields, mainly in Russia and Eastern Europe. A broad range of topics and issues in modern engineering are discussed, including the dynamics of machines and working processes, friction, wear and lubrication in machines, surface transport and technological machines, manufacturing engineering of industrial facilities, materials engineering, metallurgy, control systems and their industrial applications, industrial mechatronics, automation and robotics. The book gathers selected papers presented at the 4th International Conference on Industrial Engineering (ICIE), held in Moscow, Russia in May 2018. The authors are experts in various fields of engineering, and all papers have been carefully reviewed. Given its scope, the book will be of interest to a wide readership, including mechanical and production engineers, lecturers in engineering disciplines, and engineering graduates.
Power Transmissions
Author: Datong QinPublisher: CRC Press
ISBN: 1315386801
Category : Technology & Engineering
Languages : en
Pages : 2126
Book Description
This book presents papers from the International Conference on Power Transmissions 2016, held in Chongqing, China, 27th-30th October 2016. The main objective of this conference is to provide a forum for the most recent advances, addressing the challenges in modern mechanical transmissions. The conference proceedings address all aspects of gear and power transmission technology and a range of applications. The presented papers are catalogued into three main tracks, including design, simulation and testing, materials and manufacturing, and industrial applications. The design, simulation and testing track covers topics such as new methods and designs for all types of transmissions, modelling and simulation of power transmissions, strength, fatigue, dynamics and reliability of power transmissions, lubrication and sealing technologies and theories, and fault diagnosis of power transmissions. In the materials and manufacturing track, topics include new materials and heat treatment of power transmissions, new manufacturing technologies of power transmissions, improved tools to predict future demands on production systems, new technologies for ecologically sustainable productions and those which preserve natural resources, and measuring technologies of power transmissions. The proceedings also cover the novel industrial applications of power transmissions in marine, aerospace and railway contexts, wind turbines, the automotive industry, construction machinery, and robots.
Bearing Condition Monitoring Using Acoustic Emission and Vibration
Author: Tonphong KaewkongkaDevelopment of Acoustic Emission Based Condition Indicators for Full Ceramic Bearing Fault Diagnosis
Author: Mikhail ZadeVibroacoustic Condition Monitoring
Author: Czesław CempelPublisher: Prentice Hall
ISBN:
Category : Science
Languages : en
Pages : 236
Book Description
Vibration condition is a relatively new area of technology. This text opens with an outline of its origins and presents possible applications of the technique, showing the role of vibration diagnostics and vibration condition monitoring (VCM) during the machine running stage.
Detection and Diagnosis of Rolling Element Bearing Faults Using Time Encoded Signal Processing and Recognition
Author: Shukri Ali AbdusslamPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
This thesis presents a systematic study of using TESPAR (Time Encoded Signal Processing and Recognition), which presently is in use as an effective tool for speech recognition and shows great advantages in computational demands and accuracy, to develop a new technique for rolling element bearing fault detection and diagnosis. The fundamentals of rolling element bearings are presented in line with different failure modes and relevant monitoring methods in the time domain, the frequency domain, the envelope spectrum and the wavelet analysis. These reviews show that vibration measurements are a proven and widely accepted data source for bearing monitoring of machinery. This research thus has focused on developing TESPAR based approaches using vibration signals which are generated from bearings under different severities of faults located at the outer race, the inner race and the roller element. It firstly examines the theoretical basis of TESPAR and examines the diagnosis performance with a number of different simulated signals, which confirms that TESPAR based methods are able to resolve different signals by using their statistics including S-matrix, A-matrix and epoch duration, which paves a frame work to process and interpolate the bearing signal. With understandings of the insights of bearing vibrations and TESPAR approaches a signal processing framework is then suggested to analyse bearing vibration signals. It consists of a pre-processing step which removes possible noise in the signal, a TESPAR coding step which converts the signal into TESPAR representations-TESPAR streams, a feature calculation step, which produces different TESPAR statistic parameters, and finally a diagnosis step which applies common statistics to TESPAR statistic parameters to obtain required results. The TESPAR solution proposed in this thesis shows that discrimination between different bearing signal waveforms has been implemented successfully. TESPAR S- and A-Matrices were constructed for the cases tested and used together with statistical correlation to differentiate between the types of faults. However, the severities of bearing faults have been identified using another TESPAR feature called the mean absolute magnitude value calculated using epoch durations. The performance of the TESPAR approach was then evaluated against the envelope spectrum; this being the most common method for bearing condition monitoring that is conducted in two terms; the process complexity and diagnosis performance. A major contribution of this research programme is the development of a method that can provide improved detection and diagnosis of bearing fault types and severity of faults seeded into roller bearings.
Fault Detection for Sliding Bearings Using Acoustic Emission Signals and Machine Learning Methods
Author: Florian KönigMorphology-based Fault Feature Extraction and Resampling-free Fault Identification Techniques for Rolling Element Bearing Condition Monitoring
Author: Juanjuan SHIPublisher:
ISBN:
Category : University of Ottawa theses
Languages : en
Pages :
Book Description
As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induced impulses are further modulated by various low frequency signal contents. The compounded effects of interferences, background noise and the combined modulation effects make it difficult to detect bearing faults. This is further complicated by the nonstationary nature of vibration signals due to speed variations in some cases, such as the bearings in a wind turbine. As such, the main challenges in the vibration-based bearing monitoring are how to address the modulation, noise, interference, and nonstationarity matters. Over the past few decades, considerable research activities have been carried out to deal with the first three issues. Recently, the nonstationarity matter has also attracted strong interests from both industry and academic community. Nevertheless, the existing techniques still have problems (deficiencies) as listed below: (1) The existing enveloping methods for bearing fault feature extraction are often adversely affected by multiple interferences. To eliminate the effect of interferences, the prefiltering is required, which is often parameter-dependent and knowledge-demanding. The selection of proper filter parameters is challenging and even more so in a time-varying environment. (2) Even though filters are properly designed, they are of little use in handling in-band noise and interferences which are also barriers for bearing fault detection, particularly for incipient bearing faults with weak signatures. (3) Conventional approaches for bearing fault detection under constant speed are no longer applicable to the variable speed case because such speed fluctuations may cause zsmearingy of the discrete frequencies in the frequency representation. Most current methods for rotating machinery condition monitoring under time-varying speed require signal resampling based on the shaft rotating frequency. For the bearing case, the shaft rotating frequency is, however, often unavailable as it is coupled with the instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be determined without knowing the fault type. Additionally, the effectiveness of resampling-based methods is largely dependent on the accuracy of resampling procedure which, even if reliable, can complicate the entire fault detection process substantially. (4) Time-frequency analysis (TFA) has proved to be a powerful tool in analyzing nonstationary signal and moreover does not require resampling for bearing fault identification. However, the diffusion of time-frequency representation (TFR) along time and frequency axes caused by lack of energy concentration would handicap the application of the TFA. In fact, the reported TFA applications in bearing fault diagnosis are still very limited. To address the first two aforementioned problems, i.e., (1) and (2), for constant speed cases, two morphology-based methods are proposed to extract bearing fault feature without prefiltering. Another two methods are developed to specifically handle the remaining problems for the bearing fault detection under time-varying speed conditions. These methods are itemized as follows: (1) An efficient enveloping method based on signal Fractal Dimension (FD) for bearing fault feature extraction without prefiltering, (2) A signal decomposition technique based on oscillatory behaviors for noise reduction and interferences removal (including in-band ones), (3) A prefiltering-free and resampling-free approach for bearing fault diagnosis under variable speed condition via the joint application of FD-based envelope demodulation and generalized demodulation (GD), and (4) A combined dual-demodulation transform (DDT) and synchrosqueezing approach for TFR energy concentration level enhancement and bearing fault identification. With respect to constant speed cases, the FD-based enveloping method, where a short time Fractal dimension (STFD) transform is proposed, can suppress interferences and highlight the fault-induced impulsive signature by transforming the vibration signal into a STFD representation. Its effectiveness, however, deteriorates with the increased complexity of the interference frequencies, particularly for multiple interferences with high frequencies. As such, the second method, which isolates fault-induced transients from interferences and noise via oscillatory behavior analysis, is then developed to complement the FD-based enveloping approach. Both methods are independent of frequency information and free from prefiltering, hence eliminating the tedious process for filter parameter specification. The in-band vibration interferences can also be suppressed mainly by the second approach. For the nonstationary cases, a prefiltering-free and resampling-free strategy is developed via the joint application of STFD and GD, from which a resampling-free order spectrum can be derived. This order spectrum can effectively reveal not only the existence of a fault but also its location. However, the success of this method relies largely on an effective enveloping technique. To address this matter and at the same time to exploit the advantages of TFA in nonstationary signal analysis, a TFA technique, involving dual demodulations and an iterative process, is developed and innovatively applied to bearing fault identification. The proposed methods have been validated using both simulation and experimental data collected in our lab. The test results have shown that the first two methods can effectively extract fault signatures, remove the interferences (including in-band ones) without prefiltering, and detect fault types from vibration signals for constant speed cases. The last two have shown to be effective in detecting faults and discern fault types from vibration data collected under variable speed conditions without resampling and prefiltering.
Vibration-based Condition Monitoring
Author: Robert Bond RandallPublisher: John Wiley & Sons
ISBN: 0470977582
Category : Technology & Engineering
Languages : en
Pages : 409
Book Description
"Without doubt the best modern and up-to-date text on the topic, wirtten by one of the world leading experts in the field. Should be on the desk of any practitioner or researcher involved in the field of Machine Condition Monitoring" Simon Braun, Israel Institute of Technology Explaining complex ideas in an easy to understand way, Vibration-based Condition Monitoring provides a comprehensive survey of the application of vibration analysis to the condition monitoring of machines. Reflecting the natural progression of these systems by presenting the fundamental material and then moving onto detection, diagnosis and prognosis, Randall presents classic and state-of-the-art research results that cover vibration signals from rotating and reciprocating machines; basic signal processing techniques; fault detection; diagnostic techniques, and prognostics. Developed out of notes for a course in machine condition monitoring given by Robert Bond Randall over ten years at the University of New South Wales, Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications is essential reading for graduate and postgraduate students/ researchers in machine condition monitoring and diagnostics as well as condition monitoring practitioners and machine manufacturers who want to include a machine monitoring service with their product. Includes a number of exercises for each chapter, many based on Matlab, to illustrate basic points as well as to facilitate the use of the book as a textbook for courses in the topic. Accompanied by a website www.wiley.com/go/randall housing exercises along with data sets and implementation code in Matlab for some of the methods as well as other pedagogical aids. Authored by an internationally recognised authority in the area of condition monitoring.