30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Predictive condition monitoring of industrial systems for improved maintenance and operation

Maintenance strategies based on condition monitoring of the different machines and devices in an industrial process can minimize downtime, increase the safety of plant operations and help in the process of decision-taking for control and maintenance actions in order to reduce maintenance and operating costs. Multivariate statistical methods are widely used for process condition monitoring in modern industrial sites due to the quantity of data available and the difficulties of building analytical models in complex facilities. Nevertheless, the performance of these methodologies is still far away from being ideal, due to different issues such as process nonlinearities or varying operational conditions. In addition application of the latest approaches developed for process monitoring is not widely extended in real industry. The aim of this investigation is to develop new and improve existing methodologies for predictive condition monitoring through the use of multivariate statistical methods. The research focuses on demonstrating the applicability of multivariate algorithms in real complex cases, the improvement of these methods in terms of fault detection and diagnosis by means of data fusion and the estimation of process performance degradation caused by faults.Marie Curi

Condition monitoring of helical gearboxes based on the advanced analysis of vibration signals

Condition monitoring of rotating machinery and machine systems has attracted extensive researches, particularly the detection and diagnosis of machine faults in their early stages to minimise maintenance cost and avoid catastrophic breakdowns and human injuries. As an efficient mechanical system, helical gearbox has been widely used in rotating machinery such as wind turbines, helicopters, compressors and internal combustion engines and hence its vibration condition monitoring is attracting substantial research attention worldwide. However, the vibration signals from a gearbox are usually contaminated by background noise and influenced by operating conditions. It is usually difficult to obtain symptoms of faults at the early stage of a fault. This study focus on developing effective approaches to the detection of early stage faults in an industrial helical gearbox. In particular, continuous wavelet transformation (CWT) has been investigated in order to select an optimal wavelet to effectively represent the vibration signals for both noise reduction and fault signature extraction. To achieve this aim, time synchronous average (TSA) is used as a tool for preliminary noise reduction and mathematical models of a gearbox transmission system is developed for characterising fault signatures. The performance of three different wavelet families was compared and henceforth a criterion and method for the selection of the most discerning has been established. It has been found that the wavelet that gives the highest RMS value for the baseline vibration signal will show the greatest difference between baseline and gearbox vibration with a fault presence. Comparison of the three wavelets families shows that the Daubechies order 1 can give best performance for feature extraction and fault detection and fault quantification. However, there are limitations that undermine CWT application to fault detection, in particular the difficulty in selecting a suitable wavelet function. A major contribution of this research programme is to demonstrate a possible route on how to overcome this deficiency. An adaptive Morlet wavelet transform method has been proposed based on information entropy optimization for analysing the vibration signal and detecting and quantifying the faults seeded into the helical gearbox. This research has also developed a nonlinear dynamic model of the two-stage helical gearbox involving time–varying mesh stiffness and transmission error. Based on experimental data collected with different operating loads and the simulating results vibration signatures for gear faults are fully understood and hence confirms the CWT based scheme for signal enhancement. These results also indicate that the dynamic model can be used in studying gear faults and would be useful in developing gear fault monitoring techniques

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Condition Monitoring and Fault Diagnosis of a Multi-Stage Gear Transmission Using Vibro-acoustic Signals

Gearbox condition monitoring(CM) plays a vital role in ensuring the reliability and operational efficiency of a wide range of industrial facilities such as wind turbines and helicopters. Many technologies have been investigated intensively for more accurate CM of rotating machines with using vibro-acoustic signature analysis. However, a comparison of CM performances between surface vibrations and airborne acoustics has not been carried out with the use of emerging signal processing techniques. This research has focused on a symmetric evaluation of CM performances using vibrations obtained from the surface of a multi stage gearbox housing and the airborne sound obtained remotely but close to the gearbox, in conjunction with state of the art signal processing techniques, in order to provide efficient and effective CM for gear transmissions subject to gradual and progressive deteriorations. By completing the comparative studies, this research has resulted in a number of new findings that show significant contributions to knowledge which are detailed as follows. In general, through a comprehensive review of the advancement in the subject, the research has been carried out by integrating an improved dynamic modelling, more realistic experiment verification and more advanced signal processing approaches. The improved modelling has led to an in-depth understanding of the nonlinear modulation in vibro-acoustic signals due to wear effects. Thereafter, Time Synchronous Average (TSA) and Modulation Signal Bispectrum (MSB) are identified to be the most promising signal processing methods to fulfil the evaluation because of their unique properties of simultaneous noise reduction and modulation enhancement. The more realistic tests have demonstrated that arun-to-failure test is necessary to develop effective diagnostic tools as it produces datasets from gear transmissions where deterioration naturally progresses over a long operation, rather than faults created artificially to gear systems, as is common in the majority of studies and the results unreliable. Particularly, the evaluation studies have clarified a number of key issues in the realisation of gearbox diagnostics based on TSA and MSB analysis of the vibrations from two accelerometers and acoustics from two microphones in monitoring the run-to-failure process, which showed slight gear wear of two back-to-back multiple stage helical gearboxes under variable load and speed operations. TSA analysis of vibration signals and acoustic signals allows for accurate monitoring and diagnosis results of the gradual deterioration in the lower speed transmission of both the tested gearboxes. However, it cannot give the correct indication of the higher speed stages in the second gearbox as the reference angle signal is too erroneous due to the distortion of long transmission trains. In addition, acoustic signals can indicate that there is a small determination in the higher speed transmission of the first gearbox. The MSB analysis of vibration signals and sound signals allows for the gathering of more corrective monitoring and diagnostic results of the deterioration in the four stages of transmissions of the two tested gearboxes. MSB magnitudes of both the two lower speed transmissions show monotonic increases with operational time and the increments over a longer period are in excess of three times higher than the baselines, the deteriorations are therefore regarded as severe. For the two higher speed transmissions, the MSB of vibrations and acoustics illustrates small deteriorations in the latter operating hours. Comparatively, acoustic signal based diagnostics can out-perform vibration as it can provide an early indication of deteriorations and correct diagnosis of the faults as microphones perceive a large area of dynamic responses from gearbox housing whereas accelerometers collect a very localised response which can be distorted by transmission paths. In addition, MSB analysis can out-perform conventional TSA as it maintains all diagnostic information regarding the rotating systems and can be implemented without any additional reference channels

Unsupervised Methods for Condition-Based Maintenance in Non-Stationary Operating Conditions

Maintenance and operation of modern dynamic engineering systems requires the use of robust maintenance strategies that are reliable under uncertainty. One such strategy is condition-based maintenance (CBM), in which maintenance actions are determined based on the current health of the system. The CBM framework integrates fault detection and forecasting in the form of degradation modeling to provide real-time reliability, as well as valuable insight towards the future health of the system. Coupled with a modern information platform such as Internet-of-Things (IoT), CBM can deliver these critical functionalities at scale. The increasingly complex design and operation of engineering systems has introduced novel problems to CBM. Characteristics of these systems - such as the unavailability of historical data, or highly dynamic operating behaviour - has rendered many existing solutions infeasible. These problems have motivated the development of new and self-sufficient - or in other words - unsupervised CBM solutions. The issue, however, is that many of the necessary methods required by such frameworks have yet to be proposed within the literature. Key gaps pertaining to the lack of suitable unsupervised approaches for the pre-processing of non-stationary vibration signals, parameter estimation for fault detection, and degradation threshold estimation, need to be addressed in order to achieve an effective implementation. The main objective of this thesis is to propose set of three novel approaches to address each of the aforementioned knowledge gaps. A non-parametric pre-processing and spectral analysis approach, termed spectral mean shift clustering (S-MSC) - which applies mean shift clustering (MSC) to the short time Fourier transform (STFT) power spectrum for simultaneous de-noising and extraction of time-varying harmonic components - is proposed for the autonomous analysis of non-stationary vibration signals. A second pre-processing approach, termed Gaussian mixture model operating state decomposition (GMM-OSD) - which uses GMMs to cluster multi-modal vibration signals by their respective, unknown operating states - is proposed to address multi-modal non-stationarity. Applied in conjunction with S-MSC, these two approaches form a robust and unsupervised pre-processing framework tailored to the types of signals found in modern engineering systems. The final approach proposed in this thesis is a degradation detection and fault prediction framework, termed the Bayesian one class support vector machine (B-OCSVM), which tackles the key knowledge gaps pertaining to unsupervised parameter and degradation threshold estimation by re-framing the traditional fault detection and degradation modeling problem as a degradation detection and fault prediction problem. Validation of the three aforementioned approaches is performed across a wide range of machinery vibration data sets and applications, including data obtained from two full-scale field pilots located at Toronto Pearson International Airport. The first of which is located on the gearbox of the LINK Automated People Mover (APM) train at Toronto Pearson International Airport; and, the second which is located on a subset of passenger boarding tunnel pre-conditioned air units (PCA) in Terminal 1 of Pearson airport. Results from validation found that the proposed pre-processing approaches and combined pre-processing framework provides a robust and computationally efficient and robust methodology for the analysis of non-stationary vibration signals in unsupervised CBM. Validation of the B-OCSVM framework showed that the proposed parameter estimation approaches enables the earlier detection of the degradation process compared to existing approaches, and the proposed degradation threshold provides a reasonable estimate of the fault manifestation point. Holistically, the approaches proposed in thesis provide a crucial step forward towards the effective implementation of unsupervised CBM in complex, modern engineering systems

Aeronautical engineering: A continuing bibliography with indexes (supplement 292)

This bibliography lists 675 reports, articles, and other documents recently introduced into the NASA scientific and technical information system database. Subject coverage includes the following: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Marine Engines Performance and Emissions

This book contains a collection of peer-review scientific papers about marine engines’ performance and emissions. These papers were carefully selected for the “Marine Engines Performance and Emissions” Special Issue of the Journal of Marine Science and Engineering. Recent advancements in engine technology have allowed designers to reduce emissions and improve performance. Nevertheless, further efforts are needed to comply with the ever increased emission legislations. This book was conceived for people interested in marine engines. This information concerning recent developments may be helpful to academics, researchers, and professionals engaged in the field of marine engineering