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This project focuses on the fault observability problem and its impact on plant performance and profitability. The study has been conducted along two main directions. First, a technique has been developed to detect and diagnose faulty situations that could not be observed by previously reported methods. The technique is demonstrated through a subset of faults typically considered for the Tennessee Eastman Process (TEP); which have been found unobservable in all previous studies. The proposed strategy combines the cumulative sum (CUSUM) of the process measurements with Principal Component Analysis (PCA). The CUSUM is used to enhance faults under conditions of small fault/signal to noise ratio while the use of PCA facilitates the filtering of noise in the presence of highly correlated data. Multivariate indices, namely, T2 and Q statistics based on the cumulative sums of all available measurements were used for observing these faults. The ARLo.c was proposed as a statistical metric to quantify fault observability. Following the faults detection, the problem of fault isolation is treated. It is shown that for the particular faults considered in the TEP problem, the contribution plots are not able to properly isolate the faults under consideration. This motivates the use of the CUSUM based PCA technique previously used for detection, for unambiguously diagnose the faults. The diagnosis scheme is performed by constructing a family of CUSUM based PCA models corresponding to each fault and then testing whether the statistical thresholds related to a particular faulty model is exceeded or not, hence, indicating occurrence or absence of the corresponding fault. Although the CUSUM based techniques were found successful in detecting abnormal situations as well as isolating the faults, long time intervals were required for both detection and diagnosis. The potential economic impact of these resulting delays motivates the second main objective of this project. More specifically, a methodology to quantify the potential economical loss due to unobserved faults when standard statistical monitoring charts are used is developed. Since most of the chemical and petrochemical plants are operated under closed loop scheme, the interaction of the control is also explicitly considered. An optimization problem is formulated to search for the optimal tradeoff between fault observability and closed loop performance. This optimization problem is solved in the frequency domain by using approximate closed loop transfer function models and in the time domain using a simulation based approach. The optimization in the time domain is applied to the TEP to solve for the optimal tuning parameters of the controllers that minimize an economic cost of the process.

Electric motors are used widely in industrial manufacturing plants. Bearing faults, insulation faults, and rotor faults are the major causes of electric motor failures. Based on the line current analysis, this dissertation mainly deals with the low cost incipient fault detection of inverter-fed driven motors. Basically, low order inverter harmonics contributions to fault diagnosis, a motor drive embedded condition monitoring method, analysis of motor fault signatures in noisy line current, and a few specific applications of proposed methods are studied in detail. First, the effects of inverter harmonics on motor current fault signatures are analyzed in detail. The introduced fault signatures due to harmonics provide additional information about the motor faults and enhance the reliability of fault decisions. It is theoretically and experimentally shown that the extended fault signatures caused by the inverter harmonics are similar and comparable to those generated by the fundamental harmonic on the line current. In the next chapter, the reference frame theory is proposed as a powerful toolbox to find the exact magnitude and phase quantities of specific fault signatures in real time. The faulty motors are experimentally tested both offline, using data acquisition system, and online, employing the TMS320F2812 DSP to prove the effectiveness of the proposed tool. In addition to reference frame theory, another digital signal processor (DSP)-based phasesensitive motor fault signature detection is presented in the following chapter. This method has a powerful line current noise suppression capability while detecting the fault signatures. It is experimentally shown that the proposed method can determine the normalized magnitude and phase information of the fault signatures even in the presence of significant noise. Finally, a signal processing based fault diagnosis scheme for on-board diagnosis of rotor asymmetry at start-up and idle mode is presented. It is quite challenging to obtain these regular test conditions for long enough time during daily vehicle operations. In addition, automobile vibrations cause a non-uniform air-gap motor operation which directly affects the inductances of electric motor and results quite noisy current spectrum. The proposed method overcomes the challenges like aforementioned ones simply by testing the rotor asymmetry at zero speed.

This project focuses on the fault observability problem and its impact on plant performance and profitability. The study has been conducted along two main directions. First, a technique has been developed to detect and diagnose faulty situations that could not be observed by previously reported methods. The technique is demonstrated through a subset of faults typically considered for the Tennessee Eastman Process (TEP); which have been found unobservable in all previous studies. The proposed strategy combines the cumulative sum (CUSUM) of the process measurements with Principal Component Analysis (PCA). The CUSUM is used to enhance faults under conditions of small fault/signal to noise ratio while the use of PCA facilitates the filtering of noise in the presence of highly correlated data. Multivariate indices, namely, T2 and Q statistics based on the cumulative sums of all available measurements were used for observing these faults. The ARLo.c was proposed as a statistical metric to quantify fault observability. Following the faults detection, the problem of fault isolation is treated. It is shown that for the particular faults considered in the TEP problem, the contribution plots are not able to properly isolate the faults under consideration. This motivates the use of the CUSUM based PCA technique previously used for detection, for unambiguously diagnose the faults. The diagnosis scheme is performed by constructing a family of CUSUM based PCA models corresponding to each fault and then testing whether the statistical thresholds related to a particular faulty model is exceeded or not, hence, indicating occurrence or absence of the corresponding fault. Although the CUSUM based techniques were found successful in detecting abnormal situations as well as isolating the faults, long time intervals were required for both detection and diagnosis. The potential economic impact of these resulting delays motivates the second main objective of this project. More specifically, a methodology to quantify the potential economical loss due to unobserved faults when standard statistical monitoring charts are used is developed. Since most of the chemical and petrochemical plants are operated under closed loop scheme, the interaction of the control is also explicitly considered. An optimization problem is formulated to search for the optimal tradeoff between fault observability and closed loop performance. This optimization problem is solved in the frequency domain by using approximate closed loop transfer function models and in the time domain using a simulation based approach. The optimization in the time domain is applied to the TEP to solve for the optimal tuning parameters of the controllers that minimize an economic cost of the process.

The control and protection system of early nuclear power plants (Generation II) have been designed and built on the then reliable analog system. Technology has evolved in recent times and digital system has replaced most analog technology in most industries. Due to safety precautions and robust licensing requirements in the nuclear industry, the analog and digital system works concurrent to each other in most control and protection systems of nuclear power plants. Due to the ageing, regular maintenance and intermittent operation, the analog plant system often gives faulty signals. The objective of this thesis is to simulate a transient using a simulator to reduce the effects of system faults on the nuclear plant control and protection system, by detecting the faults early. The following steps will be performed: • validating the simulator measurements by simulating a normal operation, • detecting faults early on in the system These can be performed by resorting to a model that generates estimates of the correct sensors signal values based on actual readings and correlations among them. The next step can be performed by a fault detection module which determines early whether or not the plant systems are behaving normally and detects the fault. (Baraldi P. et al, 2010.) / Thesis (MSc (Engineering Sciences in Nuclear Engineering))--North-West University, Potchefstroom Campus, 2013.

The control and protection system of early nuclear power plants (Generation II) have been designed and built on the then reliable analog system. Technology has evolved in recent times and digital system has replaced most analog technology in most industries. Due to safety precautions and robust licensing requirements in the nuclear industry, the analog and digital system works concurrent to each other in most control and protection systems of nuclear power plants. Due to the ageing, regular maintenance and intermittent operation, the analog plant system often gives faulty signals. The objective of this thesis is to simulate a transient using a simulator to reduce the effects of system faults on the nuclear plant control and protection system, by detecting the faults early. The following steps will be performed: • validating the simulator measurements by simulating a normal operation, • detecting faults early on in the system These can be performed by resorting to a model that generates estimates of the correct sensors signal values based on actual readings and correlations among them. The next step can be performed by a fault detection module which determines early whether or not the plant systems are behaving normally and detects the fault. (Baraldi P. et al, 2010.) / Thesis (MSc (Engineering Sciences in Nuclear Engineering))--North-West University, Potchefstroom Campus, 2013.

Hoists and cranes exist in many contexts around the world, often carrying veryheavy loads. The safety for the user and bystanders is of utmost importance. Thisthesis investigates whether it is possible to perform fault detection on a systemlevel, measuring the inputs and outputs of the system without introducing newsensors. The possibility of detecting dangerous faults while letting safe faultspass is also examined.A mathematical greybox model is developed and the unknown parametersare estimated using data from a labscale test crane. Validation is then performedwith other datasets to check the accuracy of the model. A linear observer of thesystem states is created using the model. Simulated fault injections are made,and different fault detection methods are applied to the residuals created withthe observer. The results show that dangerous faults in the system or the sensorsthemselves are detectable, while safe faults are disregarded in many cases.The idea of performing model-based fault detection from a system point ofview shows potential, and continued investigation is recommended.

Master of Science / Department of Computing and Information Sciences / Mitchell L. Neilsen / Safety-critical systems embedded in avionics and automotive systems are becoming increasing complex. Components with different requirements typically share a common distributed platform for communication. To accommodate varied requirements, many of these distributed real-time systems use FlexRay communication network. FlexRay supports both time triggered and event-triggered communications. In such systems, it is vital to establish a consistent view of all the associated processes to handle fault-tolerance. This task can be accomplished through the use of a Process Group Membership Protocol. This protocol must provide a high level of assurance that it operates correctly. In this thesis, we provide for the verification of one such protocol using Model Checking. Through this verification, we found that the protocol may remove nodes from the group of operational nodes in the communicating network at a fast rate. This may lead to exhaustion of the system resources by the protocol, hampering system performance. We determine allowable rates of failure that do not hamper system performance.

FPGAs are attractive devices as they enable the designer to make changes to the system during its lifetime. This is important in the early stages of development when all the details of the final system might not be known yet. In a research environment like at CERN there are many FPGAs used for this very reason and also because they enable high speed communication and processing. The biggest problem at CERN is that the systems might have to operate in a radioactive envi- ronment which is very harsh on electronics. ASICs can be designed to withstand high levels of radiation and are used in many places but they are expensive in terms of cost and time and they are not very flexible. There is therefore a need to understand if it is possible to use FPGAs in these places or what needs to be done to make it possible. Mitigation techniques can be used to avoid that a fault caused by radiation is disrupting the system. How this can be done and the importance of under- standing the underlying architecture of the FPGA is discussed in this thesis. A simulation tool used for injecting faults into the design is proposed in order to verify that the techniques used are working as expected which might not always be the case. The methods used during simulation which provided the best protec- tion against faults is added to a system design which is implemented on a flash based FPGA mounted on a board. This board was installed in the CERN Proton Synchrotron for 99 days during which the system was continuously monitored. During this time 11 faults were detected and the system was still functional at the end of the test. The result from the simulation and hardware test shows that with reasonable effort it is possible to use commercially available FPGAs in a radioactive environment.

Rotating machinery is a critical asset of industrial plants worldwide. Bearings and gearboxes are two of the most common components found in rotating machinery of industrial plants. The malfunction of bearings and gearboxes lead the machine to fail and often these failures occur catastrophically leading to personnel injuries. Therefore it is of high importance to identify the deterioration at an early stage. Among the techniques applied to detect damage in rotating machinery, acoustic emission has been a prevalent field of research for its potential to detect defects at an earlier stage than other more established techniques such as vibration analysis and oil analysis. However, to reliably detect the fault at an early stage de-noising techniques often must be applied to reduce the AE noise generated by neighbouring components and normal component operation. For this purpose a novel signal processing algorithm has been developed combining Wavelet Packets as a pre-processor, Hilbert Transform, Autocorrelation function and Fast Fourier transform. The combination of these techniques allows identification of g repetitive patterns in the AE signal that are attributable to bearing and gear damage. The enhancement for early stage defect detection in bearings and gears provided by this method is beneficial in planning maintenance in advance, reducing machinery down-time and consequently reducing the costs associated with bearing breakdown. The effectiveness of the proposed method has been investigated experimentally using seeded and naturally developed defects in gears and bearings. In addition, research into the optimal Wavelet Packet node that offers the best de-noising results has been performed showing that the 250-750 kHz band gives the best SNR results. The detection of shaft angular misalignment using Acoustic Emission has been investigated and compared with acceleration spectra. The results obtained show enhancements of AE in detection shaft angular misalignment over vibration analysis in SNR and stability with varying operational conditions.