The development of condition monitoring and fault diagnosis systems for wind turbines has received considerable attention in recent years. With wind playing an increasing part in Canada’s electricity demand from renewable resources, installations of new wind turbines are experiencing significant growth in the region. Hence, there is a need for efficient condition monitoring and fault diagnosis systems for wind turbines. Gearbox, as one of the highest risk elements in wind turbines, is responsible for smooth operation of wind turbines. Moreover, the availability of the whole system depends on the serviceability of the gearbox. This work presents signal processing and soft computing techniques to increase the detection and diagnosis capabilities of wind turbine gearbox monitoring systems based on vibration signal analysis. Although various vibration based fault detection and diagnosis techniques for gearboxes exist in the literature, it is still a difficult task especially because of huge background noise and a large solution search space in real world applications. The objective of this work is to develop a novel, intelligent system for reliable and real time monitoring of wind turbine gearboxes. The developed system incorporates three major processes that include detecting the faults, extracting the features, and making the decisions. The fault detection process uses intelligent filtering techniques to extract faulty information buried in huge background noise. The feature extraction process extracts fault-sensitive and vibration based transient features that best describe the health of the gearboxes. The decision making module implements probabilistic decision theory based on Bayesian inference. This module also devises an intelligent decision theory based on fuzzy logic and fault semantic network. Experimental data from a gearbox test rig and real world data from wind turbines are used to verify the viability, reliability, and robustness of the methods developed in this thesis. The experimental test rig operates at various speeds and allows the implementation of different faults in gearboxes such as gear tooth crack, tooth breakage, bearing faults, iv and shaft misalignment. The application of hybrid conventional and evolutionary optimization techniques to enhance the performance of the existing filtering and fault detection methods in this domain is demonstrated. Efforts have been made to decrease the processing time in the fault detection process and to make it suitable for the real world applications. As compared to classic evolutionary optimization framework, considerable improvement in speed has been achieved with no degradation in the quality of results. The novel features extraction methods developed in this thesis recognize the different faulty signatures in the vibration signals and estimate their severity under different operating conditions. Finally, this work also demonstrates the application of intelligent decision support methods for fault diagnosis in gearboxes. / UOIT

Condition monitoring of hydraulic systems is an area that has grown substantially in the last few decades. This thesis presents a scheme that automatically generates the fault symptoms by on-line processing of raw sensor data from a real test rig. The main purposes of implementing condition monitoring in hydraulic systems are to increase productivity, decrease maintenance costs and increase safety. Since such systems are widely used in industry and becoming more complex in function, reliability of the systems must be supported by an efficient monitoring and maintenance scheme. This work proposes an accurate state space model together with a novel model-based fault diagnosis methodology. The test rig has been fabricated in the Process Automation and Robotics Laboratory at UBC. First, a state space model of the system is derived. The parameters of the model are obtained through either experiments or direct measurements and manufacturer specifications. To validate the model, the simulated and measured states are compared. The results show that under normal operating conditions the simulation program and real system produce similar state trajectories. For the validated model, a condition monitoring scheme based on the Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and process noises are considered. The results show that the algorithm estimates the iii system states with acceptable residual errors. Therefore, the structure is verified to be employed as the fault diagnosis scheme. Five types of faults are investigated in this thesis: loss of load, dynamic friction load, the internal leakage between the two hydraulic cylinder chambers, and the external leakage at either side of the actuator. Also, for each leakage scenario, three levels of leakage are investigated in the tests. The developed UKF-based fault monitoring scheme is tested on the practical system while different fault scenarios are singly introduced to the system. A sinusoidal reference signal is used for the actuator displacement. To diagnose the occurred fault in real time, three criteria, namely residual moving average of the errors, chamber pressures, and actuator characteristics, are considered. Based on the presented experimental results and discussions, the proposed scheme can accurately diagnose the occurred faults.

Commercial building owners spent $167 billion for energy in 2006. Building commissioning services have proven to be successful in saving building energy consumption. However, the optimal energy performance obtained by commissioning may subsequently degrade. The persistence of savings is of significant interest. For commissioning persistence, two statistical approaches, Days Exceeding Threshold-Date (DET-Date) method and Days Exceeding Threshold-Outside Air Temperature (DET-Toa) method, are developed to detect abnormal whole building energy consumption, and two approaches called Cosine Similarity method and Euclidean Distance Similarity method are developed to isolate the possible fault reasons. The effectiveness of these approaches is demonstrated and compared through tests in simulation and real buildings. The impacts of the factors including calibrated simulation model accuracy, fault severity, the time of fault occurrence, reference control change magnitude setting, and fault period length are addressed in the sensitivity study. The study shows that the DET-Toa method and the Cosine Similarity method are superior and more useful for the whole building fault detection and diagnosis.

Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, due to the need to decrease the downtime on production machinery and to reduce the extent of the secondary damage caused by failures. However, little research has been done to develop gear shaft and planetary gear crack detection methods based on vibration signal analysis. In this thesis, an approach to gear shaft and planetary gear fault detection based on the application of the wavelet transform to both the time synchronously averaged (TSA) signal and residual signal is presented. Wavelet approaches themselves are sometimes inefficient for picking up the fault signal characteristic under the presence of strong noise. In this thesis, the autocovariance of maximal energy wavelet coefficients is first proposed to evaluate the gear shaft and planetary gear fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using variance, kurtosis, the application of the Kolmogorov-Smirnov test (K-S test), root mean square (RMS) , and crest factor as fault indicators with continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for residual signal, are discussed. It is demonstrated using real vibration data that the early faults in gear shafts and planetary gear can be detected and identified successfully using wavelet transforms combined with the approaches mentioned above. In the second part of the thesis, the planetary gear deterioration process from the new condition to failure is modeled as a continuous time homogeneous Markov process with three states: good, warning, and breakdown. The observation process is represented by two characteristics: variance and RMS based on the analysis of autocovariance of DWT applied to the TSA signal obtained from planetary gear vibration data. The hidden Markov model parameters are estimated by maximizing the pseudo likelihood function using the EM iterative algorithm. Then, a multivariate Bayesian control chart is applied for fault detection. It can be seen from the numerical results that the Bayesian chart performs better than the traditional Chi-square chart.

Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, due to the need to decrease the downtime on production machinery and to reduce the extent of the secondary damage caused by failures. However, little research has been done to develop gear shaft and planetary gear crack detection methods based on vibration signal analysis. In this thesis, an approach to gear shaft and planetary gear fault detection based on the application of the wavelet transform to both the time synchronously averaged (TSA) signal and residual signal is presented. Wavelet approaches themselves are sometimes inefficient for picking up the fault signal characteristic under the presence of strong noise. In this thesis, the autocovariance of maximal energy wavelet coefficients is first proposed to evaluate the gear shaft and planetary gear fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using variance, kurtosis, the application of the Kolmogorov-Smirnov test (K-S test), root mean square (RMS) , and crest factor as fault indicators with continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for residual signal, are discussed. It is demonstrated using real vibration data that the early faults in gear shafts and planetary gear can be detected and identified successfully using wavelet transforms combined with the approaches mentioned above. In the second part of the thesis, the planetary gear deterioration process from the new condition to failure is modeled as a continuous time homogeneous Markov process with three states: good, warning, and breakdown. The observation process is represented by two characteristics: variance and RMS based on the analysis of autocovariance of DWT applied to the TSA signal obtained from planetary gear vibration data. The hidden Markov model parameters are estimated by maximizing the pseudo likelihood function using the EM iterative algorithm. Then, a multivariate Bayesian control chart is applied for fault detection. It can be seen from the numerical results that the Bayesian chart performs better than the traditional Chi-square chart.

No description available.

In current distribution automations design, fault flags generated by overcurrent relays are used to detect the feeder fault section. With the integration of distributed generations (DG), fault currents could be contributed from different directions and jeopardize the fault detection function. A large fault current contributed by a DG flows from downstream of a feeder could be detected by the overcurrent relay and lead to the confusion in fault detection function. In this thesis, adjunction current measurements and fault flags are utilized to minimize the possibility of mis-identification of fault section. The structure and data flow of a Java agent development framework (JADE) is adopted for feeder fault detection, identification and service restoration (FDIR). Based on information from local measurements and other agents, the FDIR function can be better conducted by local agents. Test results indicate that multi-agent systems can be used to improve system reliability and reduce service interruption time.

Condition monitoring of hydraulic systems is an area that has grown substantially in the last few decades. This thesis presents a scheme that automatically generates the fault symptoms by on-line processing of raw sensor data from a real test rig. The main purposes of implementing condition monitoring in hydraulic systems are to increase productivity, decrease maintenance costs and increase safety. Since such systems are widely used in industry and becoming more complex in function, reliability of the systems must be supported by an efficient monitoring and maintenance scheme. This work proposes an accurate state space model together with a novel model-based fault diagnosis methodology. The test rig has been fabricated in the Process Automation and Robotics Laboratory at UBC. First, a state space model of the system is derived. The parameters of the model are obtained through either experiments or direct measurements and manufacturer specifications. To validate the model, the simulated and measured states are compared. The results show that under normal operating conditions the simulation program and real system produce similar state trajectories. For the validated model, a condition monitoring scheme based on the Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and process noises are considered. The results show that the algorithm estimates the iii system states with acceptable residual errors. Therefore, the structure is verified to be employed as the fault diagnosis scheme. Five types of faults are investigated in this thesis: loss of load, dynamic friction load, the internal leakage between the two hydraulic cylinder chambers, and the external leakage at either side of the actuator. Also, for each leakage scenario, three levels of leakage are investigated in the tests. The developed UKF-based fault monitoring scheme is tested on the practical system while different fault scenarios are singly introduced to the system. A sinusoidal reference signal is used for the actuator displacement. To diagnose the occurred fault in real time, three criteria, namely residual moving average of the errors, chamber pressures, and actuator characteristics, are considered. Based on the presented experimental results and discussions, the proposed scheme can accurately diagnose the occurred faults.

No description available.

No description available.