Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor

Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and  classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on  wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis. Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transfor

Sensorless fault diagnosis of centrifugal pumps

Analysis of electrical signatures has been in use for some time to assess the condition of induction motors. In most applications, induction motors are used to drive dynamic loads, such as pumps, fans, and blowers, by means of belts, couplers and gear-boxes. Failure of either the electric motors or the driven loads is associated with operational disruptions. The large costs associated with the resulting idle equipment and personnel can often be avoided if the degradation is detected in its early stages, prior to reaching catastrophic failure conditions. Hence the need arises for cost- effective schemes to assess not only the condition of the motor but also of the driven load. This work presents an experimentally demonstrated sensorless approach for model- based detection of three different classes of faults that frequently occur in centrifugal pumps. A fault isolation scheme is also developed to distinguish between motor re- lated and pump related faults. The proposed approach is sensorless, in the sense that no mechanical sensors are required on either the pump or the motor driving the pump. Rather, fault detection and isolation is carried out using only the line voltages and phase currents of the electric motor driving the pump, as measured through standard potential transformers (PT's) and current transformers (CT's) found in industrial switchgear. The developed fault detection and isolation scheme is insensitive to electric power supply variations. Furthermore, it does not require a priori knowledge of a motor or pump model or any detailed motor or pump design parameters; a model of the system is adaptively estimated on-line. The developed algorithms have been tested on three types of staged pump faults using data collected from a centrifugal pump connected to a 3, 3 hp induction motor. Results from these experiments indicate that the proposed model-based detection scheme effectively detects all staged faults with fault detection times comparable to those obtained from vibration analysis. In addition to the staged fault experiments, extended healthy operation reveals no false alarms by the proposed detection algorithm. The proposed fault isolation method successfully classifies faults in the motor and the pump without any mis-classification

Fault Detection of Gearbox from Inverter Signals Using Advanced Signal Processing Techniques

The gear faults are time-localized transient events so time-frequency analysis techniques (such as the Short-Time Fourier Transform, Wavelet Transform, motor current signature analysis) are widely used to deal with non-stationary and nonlinear signals. Newly developed signal processing techniques (such as empirical mode decomposition and Teager Kaiser Energy Operator) enabled the recognition of the vibration modes that coexist in the system, and to have a better understanding of the nature of the fault information contained in the vibration signal. However these methods require a lot of computational power so this paper presents a novel approach of gearbox fault detection using the inverter signals to monitor the load, rather than the motor current. The proposed technique could be used for continuous monitoring as well as on-line damage detection systems for gearbox maintenance

Vibration-based adaptive novelty detection method for monitoring faults in a kinematic chain

Postprint (published version

Power quality disturbances assessment during unintentional islanding scenarios. A contribution to voltage sag studies

This paper presents a novel voltage sag topology that occurs during an unintentional islanding operation (IO) within a distribution network (DN) due to large induction motors (IMs). When a fault occurs, following the circuit breaker (CB) fault clearing, transiently, the IMs act as generators due to their remanent kinetic energy until the CB reclosing takes place. This paper primarily contributes to voltage sag characterization. Therefore, this novel topology is presented, analytically modelled and further validated. It is worth mentioning that this voltage sag has been identified in a real DN in which events have been recorded for two years. The model validation of the proposed voltage sag is done via digital simulations with a model of the real DN implemented in Matlab considering a wide range of scenarios. Both simulations and field measurements confirm the voltage sag analytical expression presented in this paper as well as exhibiting the high accuracy achieved in the three-phase model adopted.Postprint (published version

Incremental learning for large-scale stream data and its application to cybersecurity

As many human currently depend on technologies to assist with daily tasks, there are more and more applications which have been developed to be fit in one small gadget such as smart phone and tablet. Thus, by carrying this small gadget alone, most of our tasks are able to be settled efficiently and fast. Until the end of 20th century, mobile phones are only used to call and to send short message service (sms). However, in early 21st century, a rapid revolution of communi�cation technology from mobile phone into smart phone has been seen in which the smart phone is equipped by 4G Internet line along with the telephone service provider line. Thus, the users are able to make a phone call, send messages using variety of application such as Whatsapp and Line, send email, serving websites, accessing maps and handling some daily tasks via online using online banking, online shopping and online meetings via video conferences. In previous years, if there are cases of missing children or missing cars, the victims would rely on the police investigation. But now, as easy as uploading a notification about the loss on Facebook and spread the news among Facebook users, there are more people are able to help in the search. Despite the advantages that can be obtained using these technologies, there are a group of irresponsible people who take advan�tage of current technologies for their own self-interest. Among the applications that are usually being used by almost Internet users and also are often misused by cyber criminals are email and websites. Therefore, we take this initiative to make enhancement in cyber security application to avoid the Internet users from being trapped and deceived by the trick of cyber criminals by developing detec�tion system of malicious spam email and Distributed Denial of Services (DDoS) 377$3(53867$.1781.8781$0,1$+ iii backscatter. Imagine that a notice with a logo of Mobile Phone company is received by an email informing that the customer had recently run up a large mobile phone bill. A link regarding the bill is attached for him/her to find out the details. Since, the customer thinks that the billing might be wrong, thus the link is clicked. However, the link is directed to a webpage which displays a status that currently the webpage is under construction. Then the customer closes the page and thinking of to visit the website again at other time. Unfortunately, after a single click actually a malicious file is downloaded and installed without the customer aware of it. That malicious file most probably is a Trojan that capable to steal confidential information from victim’s computer. On the next day, when the same person is using the same computer to log in the online banking, all of a sudden find out that his/her money is lost totally. This is one of a worst case scenario of malicious spam email which is usually handled by cybersecurity field. Another different case of cybersecurity is the Distributed Denial of Services (DDoS) attack. Let say, Company X is selling flowers via online in which the market is from the local and international customer. The online business of Company X is running normally as usual, until a day before mother’s day, the webpage of Company X is totally down and the prospective customers could not open the webpage to make order to be sent specially for their beloved mother. Thus, the customers would search another company that sells the same item. The Company X server is down, most probably because of the DDoS attack where a junk traffic is sent to that company server which makes that server could not serve the request by the legitimate customers. This attack effect not only the profit of the company, but also reputation damage, regular customer turnover and productivity decline. Unfortunately, it is difficult for a normal user like us to detect malicious spam 377$ 3(53867$.1781.8781$0,1$+ email or DDoS attack with naked eyes. It is because recently the spammers and attacker had improved their strategy so that the malicious email and the DDoS packets are hardly able to be differentiated with the normal email and data packets. Once the Social Engineering is used by the spammers to create relevant email content in the malicious spam email and when a new campaign of DDoS attack is launched by the attacker, no normal users are capable to distinguish the benign and malicious email or data packets. This is where my Ph.D project comes in handy. My Ph.d is focusing on constructing a detection system of malicious spam email and DDoS attack using a large number of dataset which are obtained by a server that collect double-bounce email and darknet for malicious spam email detection system and DDoS backscatter detection system, respectively. As many up-to-date data are used during the learning, the detection system would become more robust to the latest strategy of the cybercriminal. Therefore, the scenario mentioned above can be avoided by assisting the user with important information at the user-end such as malicious spam email filter or at the server firewall. First of all, the method to learn large-scale stream data must be solved before implementing it in the detection system. Therefore, in Chapter 2, the general learning strategy of large-scale data is introduced to be used in the cybersecurity applications which are discussed in Chapter 3 and Chapter 4, respectively. One of a critical criterion of the detection system is capable to learn fast because after the learning, the updated information needs to be passed to user to avoid the user from being deceived by the cybercriminal. To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. Incremental learning has an ability to process large data in chunk and update the parameters after learning each chunk. Such type of learning keep and update only the minimum information on a classifier model. 377$3(53867$.1781.8781$0,1$+ Therefore, it requires relatively small memory and short learning time. On the other hand, batch learning is not suitable because it needs to store all training data, which consume a large memory capacity. Due to the limited memory, it is certainly impossible to process online large-scale data sequences using the batch learning. Therefore, the learning of large-scale stream data should be conducted incrementally. This dissertation contains of five chapters. In Chapter 1, the concept of in�cremental learning is briefly described and basic theories on Resource Allocating Network (RAN) and conventional data selection method are discussed in this chapter. Besides that, the overview of this dissertation is also elaborated in this chapter. In Chapter 2, we propose a new algorithm based on incremental Radial Basis Function Network (RBFN) to accelerate the learning in stream data. The data sequences are represented as a large chunk size of data given continuously within a short time. In order to learn such data, the learning should be carried out incrementally. Since it is certainly impossible to learn all data in a short pe�riod, selecting essential data from a given chunk can shorten the learning time. In our method, we select data that are located in untrained or “not well-learned” region and discard data at trained or “well-learned” region. These regions are represented by margin flag. Each region is consisted of similar data which are near to each other. To search the similar data, the well-known LSH method pro�posed by Andoni et al. is used. The LSH method indeed has proven be able to quickly find similar objects in a large database. Moreover, we utilize the LSH ʼs properties; hash value and Hash Table to further reduced the processing time. A flag as a criterion to decide whether to choose or not the training data is added in the Hash Table and is updated in each chunk sequence. Whereas, the hash value of RBF bases that is identical with the hash value of the training data is used to select the RBF bases that is near to the training data. The performance results of 377$ 3(53867$.1781.8781$0,1$+ vi the numerical simulation on nine UC Irvine (UCI) Machine Learning Repository datasets indicate that the proposed method can reduce the learning time, while keeping the similar accuracy rate to the conventional method. These results indi�cate that the proposed method can improve the RAN learning algorithm towards the large-scale stream data processing. In Chapter 3, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam emails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a malware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1, 2013 to May 10, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on 377$3(53867$.$$1781.8781$0,1$+ average over the testing period. In Chapter 4, we propose a fast Distributed Denial of Service (DDoS) backscat�ter detection system to detect DDoS backscatter from a combination of protocols and ports other than the following two labeled packets: Transmission Control Protocol (TCP) Port 80 (80/TCP) and User datagram Protocol (UDP) Port 53 (53/UDP). Usually, it is hard to detect DDoS backscatter from the unlabeled packets, where an expert is needed to analyze every packet manually. Since it is a costly approach, we propose a detection system using Resource Allocating Network (RAN) with data selection to select essential data. Using this method, the learning time is shorten, and thus, the DDoS backscatter can be detected fast. This detection system consists of two modules which are pre-processing and classifier. With the former module, the packets information are transformed into 17 feature-vectors. With the latter module, the RAN-LSH classifier is used, where only data located at untrained region are selected. The performance of the proposed detection system is evaluated using 9,968 training data from 80/TCP and 53/UDP, whereas 5,933 test data are from unlabeled packets which are col�lected from January 1st, 2013 until January 20th, 2014 at National Institute of Information and Communications Technology (NICT), Japan. The results indi�cate that detection system can detect the DDoS backscatter from both labeled and unlabeled packets with high recall and precision rate within a short time. Finally, in Chapter 5, we discussed the conclusions and the future work of our study: RAN-LSH classifier, malicious spam email detection system and DDoS backscatter detection system

RVM-based adaboost scheme for stator interturn faults of the induction motor

This paper presents an AdaBoost method based on RVM (Relevance Vector Machine) to detect and locate an interturn short circuit fault in the stator windings of IM (Induction Machine). This method is achieved through constructing an Adaboost combined with a weak RVM multiclassifier based on a binary tree, and the fault features are extracted from the three phase shifts between the line current and the phase voltage of IM by establishing a global stator faulty model. The simulation results show that, compared with other competitors, the proposed method has a higher precision and a stronger generalization capability, and it can accurately detect and locate an interturn short circuit fault, thus demonstrating the effectiveness of the proposed method

Support vector machine based classification in condition monitoring of induction motors

Continuous and trouble-free operation of induction motors is an essential part of modern power and production plants. Faults and failures of electrical machinery may cause remarkable economical losses but also highly dangerous situations. In addition to analytical and knowledge-based models, application of data-based models has established a firm position in the induction motor fault diagnostics during the last decade. For example, pattern recognition with Neural Networks (NN) is widely studied. Support Vector Machine (SVM) is a novel machine learning method introduced in early 90's. It is based on the statistical learning theory presented by V.N. Vapnik, and it has been successfully applied to numerous classification and pattern recognition problems such as text categorization, image recognition and bioinformatics. SVM based classifier is built to minimize the structural misclassification risk, whereas conventional classification techniques often apply minimization of the empirical risk. Therefore, SVM is claimed to lead enhanced generalisation properties. Further, application of SVM results in the global solution for a classification problem. Thirdly, SVM based classification is attractive, because its efficiency does not directly depend on the dimension of classified entities. This property is very useful in fault diagnostics, because the number of fault classification features does not have to be drastically limited. However, SVM has not yet been widely studied in the area of fault diagnostics. Specifically, in the condition monitoring of induction motor, it does not seem to have been considered before this research. In this thesis, a SVM based classification scheme is designed for different tasks in induction motor fault diagnostics and for partial discharge analysis of insulation condition monitoring. Several variables are compared as fault indicators, and forces on rotor are found to be important in fault detection instead of motor current that is currently widely studied. The measurement of forces is difficult, but easily measurable vibrations are directly related to the forces. Hence, vibration monitoring is considered in more detail as the medium for the motor fault diagnostics. SVM classifiers are essentially 2-class classifiers. In addition to the induction motor fault diagnostics, the results of this thesis cover various methods for coupling SVMs for carrying out a multi-class classification problem.reviewe

Faults Identification in Three-Phase Induction Motors Using Support Vector Machines

Induction motor is one of the most important motors used in industrial applications. The operating conditions may sometime lead the machine into different fault situations. The main types of external faults experienced by these motors are over loading, single phasing, unbalanced supply voltage, locked rotor, phase reversal, ground fault, under voltage and over voltage. The machine should be shut down when a fault is experienced to avoid damage and for the safety of the workers. Computer based relays monitor the machine and disconnect it during the faults. The relay logic used to identify these faults requires sophisticated signal processing techniques for fast and reliable operation. Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN) have been applied in induction motor relays. Though the ANN based methods are reliable, the selection of the ANN structures and training is time consuming. Recently it is observed that the AI techniques such as Support Vector Machines (SVM) are alleviating some of the limitations of ANN method. The objectives of this study are to develop a SVM based induction motor external faults identifier and study its performance with real-time induction motor faults data. Data collected from a 1/3 hp, 208 V three-phase squirrel cage induction motor is used in this project. Radial Bases Function Kernel is used to train and test the SVM, though the effect of other Kernel functions was also studied. The proposed SVM method uses RMS values of three-phase voltages and currents as inputs. The testing results showed the efficacy of the SVM based method for identifying the external faults experienced by 3-phase induction motors. It is observed that the performance of the SVM based method is better than the ANN based method both in model creation and testing accuracy

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