Enhanced information extraction from noisy vibration data for machinery fault detection and diagnosis

As key mechanical components, bearings and gearboxes are employed in most machines. To maintain efficient and safe operations in modern industries, their condition monitoring has received massive attention in recent years. This thesis focuses on the improvement of signal processing approaches to enhance the performance of vibration based monitoring techniques taking into account various data mechanisms and their associated periodic, impulsive, modulating, nonlinear coupling characteristics along with noise contamination. Through in-depth modelling, extensive simulations and experimental verifications upon different and combined faults that often occur in the bearings and gears of representative industrial gearbox systems, the thesis has made following main conclusions in acquiring accurate diagnostic information based on improved signal processing techniques: 1) Among a wide range of advanced approaches investigated, such as adaptive line enhancer (ALE), wavelet transforms, time synchronous averaging (TSA), Kurtogram analysis, and bispectrum representations, the modulation signal bispectrum based sideband estimator (MSB-SE) is regarded as the most powerful tool to enhance the periodic fault signatures as it has the unique property of simultaneous demodulation and noise reduction along with ease of implementation. 2) The proposed MSB-SE based robust detector can achieve optimal band selection and envelope spectrum analysis simultaneously and show more reliable results for bearing fault detection and diagnosis, compared with the popular Kurtogram analysis which highlights too much on localised impulses. 3) The proposed residual sideband analysis yields accurate and consistent diagnostic results of planetary gearboxes across wide operating conditions. This is because that the residual sidebands are much less influenced by inherent gear errors and can be enhanced by MSB analysis. 4) Combined faults in bearings and gears can be detected and separated by MSB analysis. To make the results more reliable, multiple slices of MSB-SE can be averaged to minimise redundant interferences and improve the diagnostic performance

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

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

Proceedings of COMADEM 201

Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors

In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions

A DPCA-based online fault indicator for gear faults using three-direction vibration signals

For online monitoring and identifying gear faults, a new fault indicator is proposed based on a multivariate statistical technique, dynamic principal component analysis (DPCA), under variable load conditions. In this method, a tri-axial vibration sensor is used to acquire the 3-direction vibration signals of gear in the gear box because it can pick up more abundant fault information than a single axis sensor does. By monitoring the value of the fault indicator, the running state of the gear (normal condition or faults) can be directly identified according to the set thresholds without using any other fault classification methods. To verify the effectiveness, the proposed method is applied on the QPZZ-II rotating machinery fault simulation rig in which the root crack and the tooth broken faults are introduced into the gearbox’s driving gear. Experimental results show that the fault indicator not only can effectively reveal the health state of the gear, but also is without being influenced by the load fluctuation. And, the accuracy rate of fault diagnosis is over 96 %

ADVANCED VIBRATION PROCESSING TECHNIQUES FOR CONDITION MONITORING AND QUALITY CONTROL IN I.C. ENGINES AND HARVESTING MACHINES

The topic of this thesis is the development and the implementation of advanced vibration processing techniques for machine condition monitoring and diagnostics with two fields of applications: the quality control of I.C. engines by means of cold tests, and the monitoring and control of harvesting processes. The cold test, i.e. the final test after the assembly line and before shipping the engine to the customer, consists of the final quality control of the engine in a non-combustion state. Techniques for engine condition monitoring based on the analysis of vibration signals are widely used. However, these techniques are often applied to engine tests in firing conditions. This thesis addresses the use of several signal processing tools as a means for the monitoring and the diagnosis of assembly faults through the cold test technology. Firstly, an approach based on the use of Symmetrized Dot Patterns for the visual characterization of vibration signatures is proposed in order to obtain reliable thresholds for the pass/fail decision after the cold test. Secondly, the fault identification is discussed on the basis of the cyclostationary modelling of the signals. The first-order cyclostationarity is exploited through the analysis of the Time Synchronous Average (TSA). Subsequently, secondorder cyclostationarity is analysed by means of the Wigner-Ville Distribution (WVD), Wigner-Ville Spectrum (WVS) and Mean Instantaneous Power (MIP). Moreover, Continuous Wavelet Transform (CWT) is presented and compared with the WVD and WVS. The choice of different wavelet functions and some methods for the CWT map optimization (i.e. purification method and the average across the scale vi method (TDAS)) are also considered. Moreover, the capabilities of the Instantaneous Angular Speed (IAS) in detecting assembly faults have been tested. It is worth noting that the cyclostationary and time-frequency technique capabilities have been verified for both simulated and real signals. The experimental results indicate that the image correlation of Symmetrised Dot Patterns is a good solution that can be used in the cold test technology in order to increase its efficiency and fault detection capability. Moreover, it will be proved that the first order cyclostationary analysis is able to identify the presence of assembly faults but it is not appropriate to localise the faults. The second order analysis overcomes this problem indicating the angular position of the mechanical part affected by the fault. This is achieved by means of a correlation between the results obtained from the cyclostationarity analysis and the angular position of the mechanical events. Concerning the time-frequency analysis, the WVS as well as the CWT, using both Morlet mother wavelet and TDAS method can be considered good tools to characterise the transients due to the fault events in the timefrequency domain. Thanks to this research study it is possible to understand which of the above-mentioned techniques is effective for an easy and fast quality control and for the diagnosis of the considered assembly faults. Moreover, the limits and drawbacks of both monitoring and diagnostic procedures are shown. The originality of the first part of the research mainly concerns the use of vibration measurements for the quality control of engines at the end of the assembly line while the greater part of methods used for cold test applications focuses on pressure and torque measurements. The second part of this thesis concerns the analysis of relationships between the harvesting process parameters relative to a nonconventional harvesting machine and its vibration response. Common and uncommon features extracted from a segmentation analysis have been correlated with the harvesting process efficiency in order to define the optimal monitoring feature subset. Moreover, the Discrete Wavelet Transform method is performed in order to find the vii frequency range mostly characterised by impulsive components. In addition, some outlines obtained through the vibro-acoustic analysis performed in the angular domain are also given. Two different indoor and outdoor test rigs have been built to test the machine under different setting conditions in order to evaluate their influence over the vibration response of the threshing unit. The test results are used to identify how the vibration generation is linked to the crop distribution during the threshing process. Good correlations have been obtained by analysing the concave middle radial signal and by calculating the relationships that exist between some time domain features and the efficiency parameters. These features can be assumed as good indexes in explaining the crop distribution between the rotor and the concave and, consequently, the efficiency of the process. Moreover, it will be shown that the vibroacoustic features selected are well-connected to the different sources of the concave excitation. The main original contribution of this second part concerns the use of the vibration signal as an effective way to monitor the harvesting process. It can also be considered as a proper quality control indicator for the user during field operations

Acoustic Condition Monitoring & Fault Diagnostics for Industrial Systems

Condition monitoring and fault diagnostics for industrial systems is required for cost reduction, maintenance scheduling, and reducing system failures. Catastrophic failure usually causes significant damage and may cause injury or fatality, making early and accurate fault diagnostics of paramount importance. Existing diagnostics can be improved by augmenting or replacing with acoustic measurements, which have proven advantages over more traditional vibration measurements including, earlier detection of emerging faults, increased diagnostic accuracy, remote sensors and easier setup and operation. However, industry adoption of acoustics remains in relative infancy due to vested confidence and reliance on existing measurement and, perceived difficulties with noise contamination and diagnostic accuracy. Researched acoustic monitoring examples typically employ specialist surface-mount transducers, signal amplification, and complex feature extraction and machine learning algorithms, focusing on noise rejection and fault classification. Usually, techniques are fine-tuned to maximise diagnostic performance for the given problem. The majority investigate mechanical fault modes, particularly Roller Element Bearings (REBs), owing to the mechanical impacts producing detectable acoustic waves. The first contribution of this project is a suitability study into the use of low-cost consumer-grade acoustic sensors for fault diagnostics of six different REB health conditions, comparing against vibration measurements. Experimental results demonstrate superior acoustic performance throughout but particularly at lower rotational speed and axial load. Additionally, inaccuracies caused by dynamic operational parameters (speed in this case), are minimised by novel multi-Support Vector Machine training. The project then expands on existing work to encompass diagnostics for a previously unreported electrical fault mode present on a Brush-Less Direct Current motor drive system. Commonly studied electrical faults, such as a broken rotor bar or squirrel cage, result from mechanical component damage artificially seeded and not spontaneous. Here, electrical fault modes are differentiated as faults caused by issues with the power supply, control system or software (not requiring mechanical damage or triggering intervention). An example studied here is a transient current instability, generated by non-linear interaction of the motor electrical parameters, parasitic components and digital controller realisation. Experimental trials successfully demonstrate real-time feature extraction and further validate consumer-grade sensors for industrial system diagnostics. Moreover, this marks the first known diagnosis of an electrically-seeded fault mode as defined in this work. Finally, approaching an industry-ready diagnostic system, the newly released PYNQ-Z2 Field Programmable Gate Array is used to implement the first known instance of multiple feature extraction algorithms that operate concurrently in continuous real-time. A proposed deep-learning algorithm can analyse the features to determine the optimum feature extraction combination for ongoing continuous monitoring. The proposed black-box, all-in-one solution, is capable of accurate unsupervised diagnostics on almost any application, maintaining excellent diagnostic performance. This marks a major leap forward from fine-tuned feature extraction performed offline for artificially seeded mechanical defects to multiple real-time feature extraction demonstrated on a spontaneous electrical fault mode with a versatile and adaptable system that is low-cost, readily available, with simple setup and operation. The presented concept represents an industry-ready all-in-one acoustic diagnostic solution, that is hoped to increase adoption of acoustic methods, greatly improving diagnostics and minimising catastrophic failures

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time