Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

Prognostic Approaches Using Transient Monitoring Methods

The utilization of steady state monitoring techniques has become an established means of providing diagnostic and prognostic information regarding both systems and equipment. However, steady state data is not the only, or in some cases, even the best source of information regarding the health and state of a system. Transient data has largely been overlooked as a source of system information due to the additional complexity in analyzing these types of signals. The development for algorithms and techniques to quickly, and intuitively develop generic quantification of deviations a transient signal towards the goal of prognostic predictions has until now, largely been overlooked. By quantifying and trending these shifts, an accurate measure of system heath can be established and utilized by prognostic algorithms. In fact, for some systems the elevated stress levels during transients can provide better, more clear indications of system health than those derived from steady state monitoring. This research is based on the hypothesis that equipment health signals for some failure modes are stronger during transient conditions than during steady-state because transient conditions (e.g. start-up) place greater stress on the equipment for these failure modes. From this it follows that these signals related to the system or equipment health would display more prominent indications of abnormality if one were to know the proper means to identify them. This project seeks to develop methods and conceptual models to monitor transient signals for equipment health. The purpose of this research is to assess if monitoring of transient signals could provide alternate or better indicators of incipient equipment failure prior to steady state signals. The project is focused on identifying methods, both traditional and novel, suitable to implement and test transient model monitoring in both an useful and intuitive way. By means of these techniques, it is shown that the addition information gathered during transient portions of life can be used to either to augment existing steady-state information, or in cases where such information is unavailable, be used as a primary means of developing prognostic models

Advanced Feature Extraction and Dimensionality Reduction for Unmanned Underwater Vehicle Fault Diagnosis

This paper presents a novel approach to the diagnosis of blade faults in an electric thruster motor of unmanned underwater vehicles (UUVs) under stationary operating conditions. The diagnostic approach is based on the use of discrete wavelet transforms (DWT) as a feature extraction tool and a dynamic neural network (DNN) for fault classification. The DNN classifies between healthy and faulty conditions of the trolling motor by analyzing the stator current and vibration signals. To overcome feature redundancy, which affects diagnosis reliability, the Orthogonal Fuzzy Neighbourhood Discriminant Analysis (OFNDA) approach is found to be the most effective. Four faulty conditions were analyzed under laboratory conditions, and the results obtained from experiment demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and more accurately

A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

Rolling element bearing defects are among the main reasons for the breakdown of electrical machines, and therefore, early diagnosis of these is necessary to avoid more catastrophic failure consequences. This paper presents a novel approach for identifying rolling element bearing defects in brushless DC motors under non-stationary operating conditions. Stator current and lateral vibration measurements are selected as fault indicators to extract meaningful features, using a discrete wavelet transform. These features are further reduced via the application of orthogonal fuzzy neighbourhood discriminative analysis. A recurrent neural network is then used to detect and classify the presence of bearing faults. The proposed system is implemented and tested in simulation on data collected from an experimental setup, to verify its effectiveness and reliability in accurately detecting and classifying the various faults

Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion

Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works

Statistical Wiener process model for vibration signals in accelerated aging processes of electric motors

This research describes random process modeling of the accelerated aging process based upon the mechanical degradation in induction motors. In order to show this aging effect, vibration measurements are considered at the end of each aging cycle, which gradually cause to bearing damage in the motor. In this manner, the accelerated aging study comprises seven aging stage sequentially and collected data set is presented as seven aging cycles and one initial cycle. Hence total aging process is represented by a set of the sequential vibration signals for initial and aged cases. Since the vibration signals are random values which represent the Gaussian distribution character in each period of measurement and this character can be conveyed to the next stage with a scaling of the signal related to elapsed time, the process reminds the Brownian motion or the so-called random walk and it is expected that the degradation can be described as a Wiener process. Examining the collected data proves that in the statistical manner, this compact data set reflects the properties of a non-stationary random process and it is expressed by the Wiener Process Model (WPM) as a statistical approach. This property of the process will be helpful to estimate the residual useful life (RUL) of the bearings in induction motors with high accuracy

Methods of condition monitoring and fault detection for electrical machines

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An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented