Time-efficient fault detection and diagnosis system for analog circuits

Time-efficient fault analysis and diagnosis of analog circuits are the most important prerequisites to achieve online health monitoring of electronic equipments, which are involving continuing challenges of ultra-large-scale integration, component tolerance, limited test points but multiple faults. This work reports an FPGA (field programmable gate array)-based analog fault diagnostic system by applying two-dimensional information fusion, two-port network analysis and interval math theory. The proposed system has three advantages over traditional ones. First, it possesses high processing speed and smart circuit size as the embedded algorithms execute parallel on FPGA. Second, the hardware structure has a good compatibility with other diagnostic algorithms. Third, the equipped Ethernet interface enhances its flexibility for remote monitoring and controlling. The experimental results obtained from two realistic example circuits indicate that the proposed methodology had yielded competitive performance in both diagnosis accuracy and time-effectiveness, with about 96% accuracy while within 60 ms computational time.Peer reviewedFinal Published versio

Investigation and Development of a Real-Time On-Site Condition Monitoring System for Induction Motors

This thesis presents an investigation and development of a real-time on-site condition monitoring system for induction motors. Induction motors are employed in various industries as an essential machine. In order to prevent catastrophic faults during its serviceable life, condition monitoring of induction motors is commonly used in industrial applications to maintain safety and reliability of plant operation. The current practice in condition monitoring primarily involves using various forms of mobile or portable devices, usually with a single sensor input to perform tests at regular intervals. However, such devices and monitoring services can be expensive and require an experienced operator for reliable decisions. Therefore, this thesis investigates an alternative low-cost solution for continuous condition monitoring of induction machines using multiple sensors, which can be located next to a machine under test and can provide condition information using indicator lights for quick diagnosis. The thesis provides hardware and software implementation details using an FPGA based CompactRIO platform. The CompactRIO embedded reconfigurable platform incorporates an FPGA, an analog input module, a real-time host controller and a custom-made indicator module as an on-site monitoring system. The CompactRIO custom-made indicator module utilizes bi-colour LEDs and requirements of CompactRIO MDK to display each level fault. Furthermore, a data acquisition and monitoring system was developed under LabVIEW FPGA environment and LabVIEW Real Time software. The system that has been successfully designed has had its performances and capabilities evaluated through several tests. In addition, real faults are also introduced to demonstrate the system’s performance. The results show that the CompactRIO system is capable of being implemented as condition monitoring system, especially as an early warning unit. The early warning information obtained from this system can be used as valuable data for further detailed fault assessment.Thesis (MEngSc) -- University of Adelaide, School of Electrical & Electronic Engineering, 200

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

Performance analysis techniques for multi-soft-core and many-soft-core systems

Multi-soft-core systems are a viable and interesting solution for embedded systems that need a particular tradeoff between performance, flexibility and development speed. As the growing capacity allows it, many-soft-cores are also expected to have relevance to future embedded systems. As a consequence, parallel programming methods and tools will be necessarily embraced as a part of the full system development process. Performance analysis is an important part of the development process for parallel applications. It is usually mandatory when you want to get a desired performance or to verify that the system is meeting some real-time constraints. One of the usual techniques used by the HPC community is the postmortem analysis of application traces. However, this is not easily transported to the embedded systems based on FPGA due to the resource limitations of the platforms. We propose several techniques and some hardware architectural support to be able to generate traces on multiprocessor systems based on FPGAs and use them to optimize the performance of the running applications

Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime

[EN] Transient-based methods for fault diagnosis of induction machines (IMs) are attracting a rising interest, due to their reliability and ability to adapt to a wide range of IM's working conditions. These methods compute the time-frequency (TF) distribution of the stator current, where the patterns of the related fault components can be detected. A significant amount of recent proposals in this field have focused on improving the resolution of the TF distributions, allowing a better discrimination and identification of fault harmonic components. Nevertheless, as the resolution improves, computational requirements (power computing and memory) greatly increase, restricting its implementation in low-cost devices for performing on-line fault diagnosis. To address these drawbacks, in this paper, the use of the short-frequency Fourier transform (SFFT) for fault diagnosis of induction machines working under transient regimes is proposed. The SFFT not only keeps the resolution of traditional techniques, such as the short-time Fourier transform, but also achieves a drastic reduction of computing time and memory resources, making this proposal suitable for on-line fault diagnosis. This method is theoretically introduced and experimentally validated using a laboratory test bench.This work was supported by the Spanish Ministerio de Economia y Competitividad in the framework of the Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, under Project DPI2014-60881-R. The Associate Editor coordinating the review process was Dr. Edoardo Fiorucci.Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bañó, Á.; Pineda-Sanchez, M. (2017). Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime. IEEE Transactions on Instrumentation and Measurement. 66(3):432-440. doi:10.1109/TIM.2016.2647458S43244066

Development and Experimental Analysis of Wireless High Accuracy Ultra-Wideband Localization Systems for Indoor Medical Applications

This dissertation addresses several interesting and relevant problems in the field of wireless technologies applied to medical applications and specifically problems related to ultra-wideband high accuracy localization for use in the operating room. This research is cross disciplinary in nature and fundamentally builds upon microwave engineering, software engineering, systems engineering, and biomedical engineering. A good portion of this work has been published in peer reviewed microwave engineering and biomedical engineering conferences and journals. Wireless technologies in medicine are discussed with focus on ultra-wideband positioning in orthopedic surgical navigation. Characterization of the operating room as a medium for ultra-wideband signal transmission helps define system design requirements. A discussion of the first generation positioning system provides a context for understanding the overall system architecture of the second generation ultra-wideband positioning system outlined in this dissertation. A system-level simulation framework provides a method for rapid prototyping of ultra-wideband positioning systems which takes into account all facets of the system (analog, digital, channel, experimental setup). This provides a robust framework for optimizing overall system design in realistic propagation environments. A practical approach is taken to outline the development of the second generation ultra-wideband positioning system which includes an integrated tag design and real-time dynamic tracking of multiple tags. The tag and receiver designs are outlined as well as receiver-side digital signal processing, system-level design support for multi-tag tracking, and potential error sources observed in dynamic experiments including phase center error, clock jitter and drift, and geometric position dilution of precision. An experimental analysis of the multi-tag positioning system provides insight into overall system performance including the main sources of error. A five base station experiment shows the potential of redundant base stations in improving overall dynamic accuracy. Finally, the system performance in low signal-to-noise ratio and non-line-of-sight environments is analyzed by focusing on receiver-side digitally-implemented ranging algorithms including leading-edge detection and peak detection. These technologies are aimed at use in next-generation medical systems with many applications including surgical navigation, wireless telemetry, medical asset tracking, and in vivo wireless sensors

A Labview Based Data Acquisition System for Monitoring and Analysis of Vibration Signal

Machine plays a vital role in the plant. Machine maintenance process is a very important factor for a plant, which is required regularly. Vibration monitoring and analysis is a predictive maintenance technique by which the faults can be detected in the machines. In this work, the vibration fault simulation system is developed for vibration signal monitoring. The structure of vibration monitoring system is discussed. The important components of vibration monitoring system are accelerometer transducer, charge amplifier, data acquisition card (DAQ) and computer. Data acquisition system, signal analysis and LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) are used to detect various faults which occur in the machine. For processing and analysis of vibration signal, time domain and frequency domain analysis of vibration signal is implemented in LabVIEW. From time domain analysis it is difficult to find the exact fault of the vibration signal. It is also difficult to locate the faults region and fault type. For avoiding the difficulties in time domain analysis, frequency domain analysis is performed. Spectrum analysis has provided more accurate information about the vibration signal type, signal fault region and fault extent as compared to time domain analysis. The control action for a faulty vibration signal is implemented using PID controller with different modes such as manually, automatically, using generator sine and using generator square. Automatic controller has provided better response than other controller. This controller has taken very less time to complete the control action

DESIGN OF A CUSTOM SOFTWARE APPLICATION TO MONITOR AND COMMUNICATE CNC MACHINING PROCESS INFORMATION TO AID IN CHATTER IDENTIFICATION

In any manufacturing environment, it is important to be able to monitor the Computer Numerical Control (CNC) machining process so that high quality parts can be produced in the least amount of time in order to be profitable. This involves acquiring the proper parameters needed from the machine\u27s controller, which can prove to be difficult with proprietary machine tools that tend to limit access to the internal data collected by the controller. This closed approach to controller design also means that many technological advances that have recently become prevalent in society are not being adopted in the manufacturing industry, preventing the interoperability between hardware and software components and adding to the shortcomings in communicating the necessary machining parameters to machine operators. The project described in this thesis offers a solution to some of the communication, productivity, and part quality problems in the American manufacturing industry by providing a custom software application that integrates MTConnect, an emerging interoperable data communication standard, with proprietary data acquisition tools and custom sensors to monitor and communicate CNC machining process information. The application described in this thesis was designed to aid in the identification of chatter conditions to the machine operator and to other users to take action for chatter suppression and avoidance. Chatter is an undesirable phenomenon that can reduce part quality and increase tool wear. These consequences result in higher costs to replace damaged parts and tools as well as increasing the amount of machine downtime which can reduce a company\u27s overall productivity. Once chatter is detected in the audible frequency range, damage to the workpiece has already occurred. Therefore, an early identification and communication method with the machine tool is warranted to easily monitor the machine in the event of impending dynamic part damage. This application was developed to provide a means to monitor cutting conditions to reduce and prevent chatter in the machining process and to aid in analysis to avoid subsequent unstable operating conditions. Preserving part quality and productivity in manufacturing is also dependent on accurate information provided about the specific parts involved in the machining process. In addition to monitoring the process, this application facilitates the communication of part-specific information by improving the input and tracking of part numbers, and organizes the machining process information in a central location according to the specific part. Improving the part tracking process can aid in the organization of data to analyze the machining process for increased quality in future operations. The application can also be customized for other implementations, which can benefit many different industrial manufacturing facilities as well as academics in performing experimental research. It is important for the manufacturing industry and its partners in academia to be able to bridge the communication gap to increase the knowledge of the machining process and therefore manufacturing productivity and profitability

On-line health monitoring of passive electronic components using digitally controlled power converter

This thesis presents System Identification based On-Line Health Monitoring to analyse the dynamic behaviour of the Switch-Mode Power Converter (SMPC), detect, and diagnose anomalies in passive electronic components. The anomaly detection in this research is determined by examining the change in passive component values due to degradation. Degradation, which is a long-term process, however, is characterised by inserting different component values in the power converter. The novel health-monitoring capability enables accurate detection of passive electronic components despite component variations and uncertainties and is valid for different topologies of the switch-mode power converter. The need for a novel on-line health-monitoring capability is driven by the need to improve unscheduled in-service, logistics, and engineering costs, including the requirement of Integrated Vehicle Health Management (IVHM) for electronic systems and components. The detection and diagnosis of degradations and failures within power converters is of great importance for aircraft electronic manufacturers, such as Thales, where component failures result in equipment downtime and large maintenance costs. The fact that existing techniques, including built-in-self test, use of dedicated sensors, physics-of-failure, and data-driven based health-monitoring, have yet to deliver extensive application in IVHM, provides the motivation for this research ... [cont.]