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.

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The main types of faults studied in the literature are commonly categorized as electrical faults and mechanical faults. In addition to well known faults, the performance of a diagnostic algorithm and its operational reliability in harsh environments has been another concern. In this work, the reliability of an electric motor diagnosis signal processing algorithm itself is studied in detail under harsh industrial conditions. Reliability and robustness of the diagnosis has especially been investigated under 1) potential motor feedback error; 2) noise interference to a diagnosis-relevant system; 3) ease of implementation; and 4) universal application of diagnostic scheme in industry. Low cost and flexible implementation strategies are also presented. 1) Signature-based diagnosis has been performed utilizing the speed feedback information which is used to determine fault characteristic frequency. Therefore, feedback information is required to maintain high accuracy for precise diagnosis which, in fact, is not the case in a practical industrial environment due to industrial noise interferences. In this dissertation, the performance under feedback error is analyzed in detail and error compensation algorithms are proposed. 2) Fault signatures are commonly small where the amplitude is continuously being interfered with motor noise. Even though a decision is based on the signature, the detection error will not be negligible if the signature amplitude is within or close to the noise floor because the boundary noise level non-linearly varies and, hence, is quite ambiguous. In this dissertation, the effect of noise interference is analyzed in detail and a threshold design strategy is presented to discriminate potential noise content in diagnosis. 3) The compensating procedure of speed feedback errors and electrical machine current noise, characteristics which are basically non-stationary random variables, requires an exhaustive tracking effort. In this dissertation, the effective diagnosis implementation strategy is precisely presented for digital signal processor (DSP) system application. 4) Most of the diagnosis algorithms in the literature are developed assuming specific detection conditions which makes application difficult for universal diagnosis purposes. In this dissertation, by assuming a sinusoidal fault signal and its Gaussian noise contents, a general diagnosis algorithm is derived which can be applied to any diagnostic scheme as a basic tool.

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.

The aim is to study a continuous chemical process, and then analyze the hold process of the reactor and build the models which could be trained to realize the fault diagnosis and localization in the process. An experimental system has been built to be the research base. That includes experiment part and record system. To the diagnosis and localization methods, the work presented the methods with the data-based approach, mainly the Bayesian network and RBF network based on GAAPA (Genetic Algorithm with Auto-adapted of Partial Adjustment). The data collected from the experimental system are used to train and test the models.

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Air gap eccentricity fault is one of the major faults that afflict the life and performance of rotating machines. Eccentricity fault, in the worst case, causes a stator rotor rub. Thus, a condition monitoring scheme to identify eccentricity fault at its initial stage is necessary. The most widely practised air gap monitoring schemes for synchronous machines are expensive and invasive sensors based. This work has focussed on developing an inexpensive, non-invasive, air gap monitoring technique especially for salient pole synchronous machines. Motor current signature analysis has been mostly preferred for the above mentioned purpose. By monitoring the frequency spectrum of the machine’s current, faulty condition can be isolated provided the fault specific frequency components are known beforehand. The research work, therefore, has developed a specific permeance function using binomial series for salient pole machines that can be used to identify eccentricity specific harmonic components in the line current spectrum. Then by performing the magneto-motive force – specific permeance analysis the characteristic frequency components have been predicted. In order to validate the prediction as well as to identify a trend in the variation of these harmonic components with changing levels of eccentricity, mathematical models of a three phase reluctance synchronous machine and a three phase salient pole synchronous machine based on modified winding function approach have been developed. The models have been made to incorporate static, dynamic and mixed eccentricity conditions of varying severity. Also time stepped finite element based models have been simulated in Maxwell-2D to verify the theoretical predictions. With the help of eccentrically cut bushings, experiments were then conducted in the laboratory to corroborate the proposed eccentricity detection scheme. It has been observed that non-idealities such as supply time harmonics, machine constructional asymmetry, supply voltage unbalance etc. negatively impact the diagnostic technique. Consequently, a residual estimation based fault detection scheme has been implemented successfully to distinguish eccentricity fault from healthy condition. Moreover, detection logic have been put forth to discriminate the type of eccentricity and to estimate the severity of the fault. / Graduate

Time-frequency methods are widely used tools to diagnose planetary gearbox fault under non-stationary conditions. However, the existing time-frequency methods still have some problems, such as smearing effect and cross-term interference, and these problems limit the effectiveness of the existing time-frequency methods in planetary gearbox fault diagnosis under non-stationary conditions. To address the aforementioned problems, four time-frequency methods are proposed in this thesis. As nowadays a large portion of the industrial equipment is equipped with tachometers, the first three methods are for the cases that the shaft rotational speed is easily accessible and the last method is for the cases of shaft rotational speed is not easily accessible. The proposed methods are itemized as follows: (1) The velocity synchronous short-time Fourier transform (VSSTFT), which is a type of linear transform based on the domain mappings and short-time Fourier transform to address the smear effect of the existing linear transforms under known time-varying speed conditions; (2) The velocity synchrosqueezing transform (VST), which is a type of remapping method based on the domain mapping and synchrosqueezing transform to address the smear effect of existing remapping methods under known time-varying speed conditions; (3) The velocity synchronous bilinear distribution (VSBD), which is a type of bilinear distribution based on the generalized demodulation and Cohen’s class bilinear distribution to address the smear effect and cross-term interference of existing bilinear distributions under known time-varying speed conditions and (4) The velocity synchronous linear chirplet transform (VSLCT), which is a non-parametric combined approach of linear transform and concentration-index-guided parameter determination to provide a smear-free and cross-term-free TFR under unknown time-varying speed conditions. In this work, simple algorithms are developed to avoid the signal resampling process required by the domain mappings or demodulations of the first three methods (i.e., the VSSTFT, VST and VSBD). They are designed to have different resolutions, readabilities, noise tolerances and computational efficiencies. Therefore, they are capable to adapt different application conditions. The VSLCT, as a kind of linear transform, is designed for unknown rotational speed conditions. It utilizes a set of shaft-rotational-speed-synchronous bases to address the smear problem and it is capable to dynamically determine the signal processing parameters (i.e., window length and normalized angle) to provide a clear TFR with desirable time-frequency resolution in response to condition variations. All of the proposed methods in this work are smear-free and cross-term-free, the TFRs generated by the methods are clearer and more precise compared with the existing time-frequency methods. The faults of planetary gearboxes, if any, can be diagnosed by identifying the fault-induced components from the obtained TFRs. The four methods are all newly applied to fault diagnosis. The effectiveness of them has been validated using both simulated and experimental vibration signals of planetary gearboxes collected under non-stationary conditions.

The low-frequency electromagnetic compatibility (EMC) is an increasingly important aspect in the design of practical systems to ensure the functional safety and reliability of complex products. The opportunities for using numerical techniques to predict and analyze system’s EMC are therefore of considerable interest in many industries. As the first phase of study, a proper model, including all the details of the component, was required. Therefore, the advances in EMC modeling were studied with classifying analytical and numerical models. The selected model was finite element (FE) modeling, coupled with the distributed network method, to generate the model of the converter’s components and obtain the frequency behavioral model of the converter. The method has the ability to reveal the behavior of parasitic elements and higher resonances, which have critical impacts in studying EMI problems. For the EMC and signature studies of the machine drives, the equivalent source modeling was studied. Considering the details of the multi-machine environment, including actual models, some innovation in equivalent source modeling was performed to decrease the simulation time dramatically. Several models were designed in this study and the voltage current cube model and wire model have the best result. The GA-based PSO method is used as the optimization process. Superposition and suppression of the fields in coupling the components were also studied and verified. The simulation time of the equivalent model is 80-100 times lower than the detailed model. All tests were verified experimentally. As the application of EMC and signature study, the fault diagnosis and condition monitoring of an induction motor drive was developed using radiated fields. In addition to experimental tests, the 3DFE analysis was coupled with circuit-based software to implement the incipient fault cases. The identification was implemented using ANN for seventy various faulty cases. The simulation results were verified experimentally. Finally, the identification of the types of power components were implemented. The results show that it is possible to identify the type of components, as well as the faulty components, by comparing the amplitudes of their stray field harmonics. The identification using the stray fields is nondestructive and can be used for the setups that cannot go offline and be dismantled

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