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

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Condition Monitoring Methods for Large, Low-speed Bearings

In all industrial production plants, well-functioning machines and systems are required for sustained and safe operation. However, asset performance degrades over time and may lead to reduced effiency, poor product quality, secondary damage to other assets or even complete failure and unplanned downtime of critical systems. Besides the potential safety hazards from machine failure, the economic consequences are large, particularly in offshore applications where repairs are difficult. This thesis focuses on large, low-speed rolling element bearings, concretized by the main swivel bearing of an offshore drilling machine. Surveys have shown that bearing failure in drilling machines is a major cause of rig downtime. Bearings have a finite lifetime, which can be estimated using formulas supplied by the bearing manufacturer. Premature failure may still occur as a result of irregularities in operating conditions and use, lubrication, mounting, contamination, or external environmental factors. On the contrary, a bearing may also exceed the expected lifetime. Compared to smaller bearings, historical failure data from large, low-speed machinery is rare. Due to the high cost of maintenance and repairs, the preferred maintenance arrangement is often condition based. Vibration measurements with accelerometers is the most common data acquisition technique. However, vibration based condition monitoring of large, low-speed bearings is challenging, due to non-stationary operating conditions, low kinetic energy and increased distance from fault to transducer. On the sensor side, this project has also investigated the usage of acoustic emission sensors for condition monitoring purposes. Roller end damage is identified as a failure mode of interest in tapered axial bearings. Early stage abrasive wear has been observed on bearings in drilling machines. The failure mode is currently only detectable upon visual inspection and potentially through wear debris in the bearing lubricant. In this thesis, multiple machine learning algorithms are developed and applied to handle the challenges of fault detection in large, low-speed bearings with little or no historical data and unknown fault signatures. The feasibility of transfer learning is demonstrated, as an approach to speed up implementation of automated fault detection systems when historical failure data is available. Variational autoencoders are proposed as a method for unsupervised dimensionality reduction and feature extraction, being useful for obtaining a health indicator with a statistical anomaly detection threshold. Data is collected from numerous experiments throughout the project. Most notably, a test was performed on a real offshore drilling machine with roller end wear in the bearing. To replicate this failure mode and aid development of condition monitoring methods, an axial bearing test rig has been designed and built as a part of the project. An overview of all experiments, methods and results are given in the thesis, with details covered in the appended papers.publishedVersio

Deep-compact-clustering based anomaly detection applied to electromechanical industrial systems

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.Peer ReviewedPostprint (published version

Healthy Monitoring and Fault Detection Outer Race Bearing in Induction Motor Using Stator Current

This paper describes a new method for monitor and detection bearing damage on induction motor. This is considered important because induction motors have an important role in the industry which functions as the main engine in the production process. This paper focuses on bearing damage because this is the most common case. the case of damaged bearing is done by giving the outer race bearing hole in three cases, 1 mm hole, 2 mm hole and 3 mm hole. Bearing damage analysis is carried out through a stator current signal on an induction motor, then the Fast Fourier transform (FFT) algorithm is used to see the characteristics of the frequency spectrum that appears in the stator current signal. High frequency analysis is applied to get clear analysis results. The results obtained by analyzing the frequency spectrum in the stator current that bearing damage indicate there is an amplitude that appears in the sideband frequenc

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.Peer ReviewedPostprint (published version

Development of effective gearbox fault diagnosis methodologies utilising various levels of prior knowledge

Effective fault diagnosis techniques are important to ensure that expensive assets such as wind turbines can operate reliably. Vibration condition monitoring data are rich with information pertaining to the dynamics of the rotating machines and are therefore popular for rotating machine diagnostics. However, vibration data do not only contain diagnostic information, but operating condition information as well. The performance of many conventional fault diagnosis techniques is impeded by inherent varying operating conditions encountered in machines such as wind turbines and draglines. Hence, it is not only important to utilise fault diagnosis techniques that are sensitive to faults, but the techniques should also be robust to changes in operating conditions. Much research has been conducted to address the many facets of gearbox fault diagnosis e.g. understanding the interactions of the components, the characteristics of the vibration signals and the development of good vibration analysis techniques. The aforementioned knowledge, as well as the availability of historical data, are regarded as prior knowledge (i.e. information that is available before inferring the condition of the machine) in this thesis. The available prior knowledge can be utilised to ensure that e ective gearbox fault diagnosis techniques are designed. Therefore, methodologies are proposed in this work which can utilise the available prior knowledge to e ectively perform fault diagnosis, i.e. detection, localisation and trending, under varying operating conditions. It is necessary to design di erent methodologies to accommodate the di erent kinds of historical data (e.g. healthy historical data or historical fault data) that can be encountered and the di erent signal analysis techniques that can be used. More speci cally, a methodology is developed to automatically detect localised gear damage under varying operating conditions without any historical data being available. The success of the methodology is attributed to the fact that the interaction between gear teeth in a similar condition results in data being generated which are statistically similar and this prior knowledge may be utilised. Therefore, a dissimilarity measure between the probability density functions of two teeth can be used to detect a gear tooth with localised gear damage. Three methodologies are also developed to utilise the available historical data from a healthy machine for gearbox fault diagnosis. Firstly, discrepancy analysis, a powerful novelty detection technique which has been used for gear diagnostics under varying operating conditions, is extended for bearing diagnostics under varying operating conditions. The suitability of time-frequency analysis techniques and di erent models are compared for discrepancy analysis as well. Secondly, a methodology is developed where the spectral coherence, a powerful second-order cyclostationary technique, is supplemented with healthy historical data for fault detection, localisation and trending. Lastly, a methodology is proposed which utilises narrowband feature extraction methods such as the kurtogram to extract a signal rich with novel information from a vibration signal. This is performed by attenuating the historical information in the signal. Sophisticated signal analysis techniques such as the squared envelope spectrum and the spectral coherence are also used on the novel signal to highlight the bene ts of utilising the novel signal as opposed to raw vibration signal for fault diagnosis. Even though a healthy state is the desired operating condition of rotating machines, fault data will become available during the operational life of the machine. Therefore, a methodology, centred around discrepancy analysis, is developed to utilise the available historical fault data and to accommodate fault data becoming available during the operation of the machine. In this investigation, it is recognised that the machine condition monitoring problem is in fact an open set recognition problem with continuous transitions between the healthy machine condition and the failure conditions. This is explicitly incorporated into the methodology and used to infer the condition of the gearbox in an open set recognition framework. This methodology uses a di erent approach to the conventional supervised machine learning techniques found in the literature. The methodologies are investigated on numerical and experimental datasets generated under varying operating conditions. The results indicate the bene ts of incorporating prior knowledge into the fault diagnosis process: the fault diagnosis techniques can be more robust to varying operating conditions, more sensitive to damage and easier to interpret by a non-expert. In summary, fault diagnosis techniques are more e ective when prior knowledge is utilised.Thesis (PhD)--University of Pretoria, 2019.Mechanical and Aeronautical EngineeringPhDUnrestricte