Fault Signature Identification for BLDC motor Drive System -A Statistical Signal Fusion Approach

A hybrid approach based on multirate signal processing and sensory data fusion is proposed for the condition monitoring and identification of fault signal signatures used in the Flight ECS (Engine Control System) unit. Though motor current signature analysis (MCSA) is widely used for fault detection now-a-days, the proposed hybrid method qualifies as one of the most powerful online/offline techniques for diagnosing the process faults. Existing approaches have some drawbacks that can degrade the performance and accuracy of a process-diagnosis system. In particular, it is very difficult to detect random stochastic noise due to the nonlinear behavior of valve controller. Using only Short Time Fourier Transform (STFT), frequency leakage and the small amplitude of the current components related to the fault can be observed, but the fault due to the controller behavior cannot be observed. Therefore, a framework of advanced multirate signal and data-processing aided with sensor fusion algorithms is proposed in this article and satisfactory results are obtained. For implementing the system, a DSP-based BLDC motor controller with three-phase inverter module (TMS 320F2812) is used and the performance of the proposed method is validated on real time data.Comment: 7 Pages, 7 figure

Advances in fuzzy rule-based system for pattern classification

Ph.DDOCTOR OF PHILOSOPH

Remote machine condition monitoring based on power supply measurements

The most widely used rotating machines in the industry are three phase alternative current (AC) induction machines. With the advances in variable speed drive (VSD) technology, they have become even more reliable than their direct current (DC) counterpart. However, inevitably these motors soon begin to fail with time due to mechanical, electrical or thermal stress hence the need for condition monitoring (CM). Condition monitoring systems help keep machines running productively by detecting potential equipment failures before it actually fails. Many condition monitoring methods exist on the market including vibration monitoring; acoustic emission monitoring, thermal monitoring, chemical monitoring, current monitoring but most of these methods require additional sensors and expensive data acquisition system on top of a specialise software tool. This all increases the cost of ownership and maintenance. For more efficient monitoring of induction motor drive systems, this research investigates an innovative remote monitoring system using existing data available in AC drives based on AC motor operating process. This research uses standard automation components already present in most automated control systems. A remote data communication platform is developed, allowing access to the control data remotely over a wireless network and internet using PLC and SCADA system. Remote machine condition monitoring is not a new idea but its application to machine monitoring based on power supply parameters indirectly measured by an inverter is new. To evaluate the basic performance of the platform, the monitoring of shaft misalignment, a typical fault in mechanical system is investigated using an in-house gearbox test rig. It has resulted in a model based detection method based on different speed and load settings against the motor current feedback read by the inverter. The results have demonstrated that the platform is reliable and effective. In addition the monitoring method can be employed to detect and diagnose different degrees of misalignment in real time. This dissertation has major contributions to knowledge which includes: Understanding of real life machine condition monitoring problems for this application, including use of wireless sensor, communication over Industrial Ethernet and network security. The use of standard automation components (PLC and SCADA) in machine condition monitoring. MSc Research (Engineering) Thesis x An improved gearbox test rig platform which has the capability of remote control, acquiring and transferring data for monitoring induction machine drive system. The presented work shows that any machine using automated components such as PLC and SCADA and incorporating motor drive systems and other actuators has the potential to use the automated components for control, condition monitoring and reporting but this will require more tests to be done using the proposed platform

Stator current signal crossing for fault diagnosis of self-excited induction generators

This paper presents a novel method for modelling and diagnosis of electrical and mechanical faults in fixed-Speed Self-Excited Induction Generators (SEIGs) operating in autonomous mode in a small-scale wind energy system. The proposed method is validated using the finite element method. After the selection of the magnetising capacitors, the self-excitation process is performed under no-load conditions. Once the stator voltage is established, a symmetrical three-phase load is connected. The fault detection method introduced here is called Stator Current Signal Crossing (SCSC). The SCSC extracts a new signal from the stator currents, that enables the detection of stator inter turn shortcircuits, broken rotor bars, and dynamic eccentricity faults in SEIGs. A spectral analysis of SCSC using the Fast Fourier Transform (FFT) algorithm is used to precisely locate the induced fault components. What sets this fault-tracking method apart from its predecessors is its exceptional ability to detect faults of any magnitude by analysing the modulation of the SCSC signal. These faults are directly identified by the presence of distinct harmonics, each indicative of a specific type of fault. This study also focuses on the SEIG in a wind energy system, whereas previous works have mainly addressed the induction machine in motor mode. In contrast, previous methods involved analysing a single current signal and isolating specific harmonics from a wide frequency range. The effectiveness of the proposed fault detection method and the self-excitation process are illustrated by simulation results and spectral analysis

Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal Processing Techniques

Producción CientíficaIn the wind industry (WI), a robust and effective maintenance system is essential. To minimize the maintenance cost, a large number of methodologies and mathematical models for predictive maintenance have been developed. Fault detection and diagnosis are carried out by processing and analyzing various types of signals, with the vibration signal predominating. In addition, most of the published proposals for wind turbine (WT) fault detection and diagnosis have used simulations and test benches. Based on previous work, this research report focuses on fault diagnosis, in this case using the electrical signal from an operating WT electric generator and applying various signal analysis and processing techniques to compare the effectiveness of each. The WT used for this research is 20 years old and works with a squirrel-cage induction generator (SCIG) which, according to the wind farm control systems, was fault-free. As a result, it has been possible to verify the feasibility of using the current signal to detect and diagnose faults through spectral analysis (SA) using a fast Fourier transform (FFT), periodogram, spectrogram, and scalogram

On the application of intelligent systems for fault diagnosis in induction motors

Muitas pesquisas estão sendo desenvolvidas buscando nos sistemas inteligentes soluções para diagnosticar falhas em máquinas elétricas. Estas falhas envolvem desde problemas elétricos, como curto-circuito numa das fases do estator, ate problemas mecânicos, como danos nos rolamentos. Dentre os sistemas inteligentes aplicados nesta área, destacam-se as redes neurais artificiais, os sistemas fuzzy, os algoritmos genéticos e os sistemas híbridos, como o neuro-fuzzy. Assim, o objetivo deste artigo é traçar um panorama geral sobre os trabalhos mais relevantes que se beneficiaram dos sistemas inteligentes nas diferentes etapas de análise e diagnóstico de falhas em motores elétricos, cuja principal contribuição está em disponibilizar diversos aspectos técnicos a fim de direcionar futuros trabalhos nesta área de aplicação

Data-Driven and Hybrid Methods for Naval Applications

The goal of this PhD thesis is to study, design and develop data analysis methods for naval applications. Data analysis is improving our ways to understand complex phenomena by profitably taking advantage of the information laying behind a collection of data. In fact, by adopting algorithms coming from the world of statistics and machine learning it is possible to extract valuable information, without requiring specific domain knowledge of the system generating the data. The application of such methods to marine contexts opens new research scenarios, since typical naval problems can now be solved with higher accuracy rates with respect to more classical techniques, based on the physical equations governing the naval system. During this study, some major naval problems have been addressed adopting state-of-the-art and novel data analysis techniques: condition-based maintenance, consisting in assets monitoring, maintenance planning, and real-time anomaly detection; energy and consumption monitoring, in order to reduce vessel consumption and gas emissions; system safety for maneuvering control and collision avoidance; components design, in order to detect possible defects at design stage. A review of the state-of-the-art of data analysis and machine learning techniques together with the preliminary results of the application of such methods to the aforementioned problems show a growing interest in these research topics and that effective data-driven solutions can be applied to the naval context. Moreover, for some applications, data-driven models have been used in conjunction with domain-dependent methods, modelling physical phenomena, in order to exploit both mechanistic knowledge of the system and available measurements. These hybrid methods are proved to provide more accurate and interpretable results with respect to both the pure physical or data-driven approaches taken singularly, thus showing that in the naval context it is possible to offer new valuable methodologies by either providing novel statistical methods or improving the state-of-the-art ones

On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines. This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids. To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and appeals to a much wider variety of electric machines connected to power electronic converters or drivers. To sum up, this PhD research has a broad beneficial impact on a wide spectrum of power-converter-fed electric machines and is thus of practical importance