Real-Time Fault Diagnosis of Permanent Magnet Synchronous Motor and Drive System

Permanent Magnet Synchronous Motors (PMSMs) have gained massive popularity in industrial applications such as electric vehicles, robotic systems, and offshore industries due to their merits of efficiency, power density, and controllability. PMSMs working in such applications are constantly exposed to electrical, thermal, and mechanical stresses, resulting in different faults such as electrical, mechanical, and magnetic faults. These faults may lead to efficiency reduction, excessive heat, and even catastrophic system breakdown if not diagnosed in time. Therefore, developing methods for real-time condition monitoring and detection of faults at early stages can substantially lower maintenance costs, downtime of the system, and productivity loss. In this dissertation, condition monitoring and detection of the three most common faults in PMSMs and drive systems, namely inter-turn short circuit, demagnetization, and sensor faults are studied. First, modeling and detection of inter-turn short circuit fault is investigated by proposing one FEM-based model, and one analytical model. In these two models, efforts are made to extract either fault indicators or adjustments for being used in combination with more complex detection methods. Subsequently, a systematic fault diagnosis of PMSM and drive system containing multiple faults based on structural analysis is presented. After implementing structural analysis and obtaining the redundant part of the PMSM and drive system, several sequential residuals are designed and implemented based on the fault terms that appear in each of the redundant sets to detect and isolate the studied faults which are applied at different time intervals. Finally, real-time detection of faults in PMSMs and drive systems by using a powerful statistical signal-processing detector such as generalized likelihood ratio test is investigated. By using generalized likelihood ratio test, a threshold was obtained based on choosing the probability of a false alarm and the probability of detection for each detector based on which decision was made to indicate the presence of the studied faults. To improve the detection and recovery delay time, a recursive cumulative GLRT with an adaptive threshold algorithm is implemented. As a result, a more processed fault indicator is achieved by this recursive algorithm that is compared to an arbitrary threshold, and a decision is made in real-time performance. The experimental results show that the statistical detector is able to efficiently detect all the unexpected faults in the presence of unknown noise and without experiencing any false alarm, proving the effectiveness of this diagnostic approach.publishedVersio

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

ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing

In industry 4.0, predictive maintenance(PM) is one of the most important applications pertaining to the Internet of Things(IoT). Machine learning is used to predict the possible failure of a machine before the actual event occurs. However, the main challenges in PM are (a) lack of enough data from failing machines, and (b) paucity of power and bandwidth to transmit sensor data to cloud throughout the lifetime of the machine. Alternatively, edge computing approaches reduce data transmission and consume low energy. In this paper, we propose Anomaly Detection based Power Saving(ADEPOS) scheme using approximate computing through the lifetime of the machine. In the beginning of the machines life, low accuracy computations are used when the machine is healthy. However, on the detection of anomalies, as time progresses, the system is switched to higher accuracy modes. We show using the NASA bearing dataset that using ADEPOS, we need 8.8X less neurons on average and based on post-layout results, the resultant energy savings are 6.4 to 6.65XComment: Submitted to ASP-DAC 2019, Japa

Simplified Automatic Fault Detection in Wind Turbine Induction Generators

This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach

Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines

International audienceCost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for failure detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed failure detection technique has been validated experimentally regarding bearing failures. Indeed, a large fraction of wind turbine downtime is due to bearing failures, particularly in the generator and gearbox

Detection and classification of turn fault and high resistance connection fault in permanent magnet machines based on zero sequence voltage

Health monitoring and fault detection are becoming more and more important in electrical machine systems due to the increasing demand for reliability. Winding turn fault is a common fault in permanent magnet machines which can cause severe damages and requires prompt detection and mitigation. High resistance connection (HRC) fault which result in phase asymmetry may also occur but does not require immediate shutdown. Thus, apart from the fault detection, the classification between the two faults is also required. In this paper, a new technique for detecting and classifying turn fault and HRC fault by utilizing both the high and low frequency components of the zero sequence voltage is proposed. The dependence on the operating conditions is minimized with the proposed fault indicators. The effectiveness of fault detection and classification has been verified by extensive experimental tests on a triple redundant fault tolerant permanent magnet assisted synchronous reluctance machine (PMA SynRM). The robustness of the turn fault detection in transient states and under no load conditions has also been demonstrated

Enhanching Security in the Future Cyber Physical Systems

Cyber Physical System (CPS) is a system where cyber and physical components work in a complex co-ordination to provide better performance. By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of a CPS. In this dissertation, security measures for different types of attacks/ faults in two CPSs, water supply system (WSS) and smart grid system, are presented. In this context, I also present my study on energy management in Smart Grid. The techniques for detecting attacks/faults in both WSS and Smart grid system adopt Kalman Filter (KF) and χ2 detector. The χ2 -detector can detect myriad of system fault- s/attacks such as Denial of Service (DoS) attack, short term and long term random attacks. However, the study shows that the χ2 -detector is unable to detect the intelligent False Data Injection attack (FDI). To overcome this limitation, I present a Euclidean detector for smart grid which can effectively detect such injection attacks. Along with detecting attack/faults I also present the isolation of the attacked/faulty nodes for smart grid. For isolation the Gen- eralized Observer Scheme (GOS) implementing Kalman Filter is used. As GOS is effective in isolating attacks/faults on a single sensor, it is unable to isolate simultaneous attacks/faults on multiple sensors. To address this issue, an Iterative Observer Scheme (IOS) is presented which is able to detect attack on multiple sensors. Since network is an integral part of the future CPSs, I also present a scheme for pre- serving privacy in the future Internet architecture, namely MobilityFirst architecture. The proposed scheme, called Anonymity in MobilityFirst (AMF), utilizes the three-tiered ap- proach to effectively exploit the inherent properties of MF Network such as Globally Unique Flat Identifier (GUID) and Global Name Resolution Service (GNRS) to provide anonymity to the users. While employing new proposed schemes in exchanging of keys between different tiers of routers to alleviate trust issues, the proposed scheme uses multiple routers in each tier to avoid collaboration amongst the routers in the three tiers to expose the end users