Fault detection and diagnosis methods for engineering systems

The main aim of this thesis is to investigate available techniques and develop a methodology for the fault detection and diagnostics for two engineering systems, namely railway point systems (RPS) and three-phase separators (TPS). The fault detection of the RPS was performed on the measured current from the motor of point operating equipment (POE). The method of One Class Support Vector Machines has been chosen as the fault detection model. Elastic similarity measures, such as edit distance with real penalties and dynamic time warping, were chosen to compare the data of POE operations. A combination of Euclidean distance and elastic similarity measures has been proposed in order to take into account the absolute values and shape properties of the two compared time series. The proposed methodology has been tested on the in-field RPS data. The results indicated that the fault detection model was able to detect abnormal values and/or shape of the time series of measured current. However, not in all cases these changes could be related to a recorded failure of RPS in the database. The fault detection of TPS was performed given the sensor readings of flow and level transmitters of TPS. The method of Bayesian Belief Networks (BBN) has been proposed to overcome the late detection of faults with the threshold based alarm technique. An approach to observe sensor readings of TPS in several adjacent time intervals and to update the prior probabilities in the BBN after inserting the sensor readings as evidence was proposed. The proposed methodology has been tested on the data obtained from a TPS simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the TPS

Extended Bow-Tie model for asset risk and reliability modeling: application to a passenger lift

A risk and reliability modelling framework for railway assets based on the Petri Net and the Bow-Tie models is proposed in this paper. A Petri Net model together with the Monte Carlo simulation is used to replicate the projected operational usage of the asset, inspection and maintenance policies and degra-dation of the asset and to estimate the future condition of the asset over time. Statistics obtained from the Petri Net are used as inputs to the Bow-Tie model, which is then used to estimate the risk of a hazardous event. The paper reports on the proposed methodology and the results of a case study of an underground passenger lift. In particular, the likelihood and the consequences of a lift getting stuck in shaft between landings are calculated

Fault detection and diagnosis methods for engineering systems

The main aim of this thesis is to investigate available techniques and develop a methodology for the fault detection and diagnostics for two engineering systems, namely railway point systems (RPS) and three-phase separators (TPS). The fault detection of the RPS was performed on the measured current from the motor of point operating equipment (POE). The method of One Class Support Vector Machines has been chosen as the fault detection model. Elastic similarity measures, such as edit distance with real penalties and dynamic time warping, were chosen to compare the data of POE operations. A combination of Euclidean distance and elastic similarity measures has been proposed in order to take into account the absolute values and shape properties of the two compared time series. The proposed methodology has been tested on the in-field RPS data. The results indicated that the fault detection model was able to detect abnormal values and/or shape of the time series of measured current. However, not in all cases these changes could be related to a recorded failure of RPS in the database. The fault detection of TPS was performed given the sensor readings of flow and level transmitters of TPS. The method of Bayesian Belief Networks (BBN) has been proposed to overcome the late detection of faults with the threshold based alarm technique. An approach to observe sensor readings of TPS in several adjacent time intervals and to update the prior probabilities in the BBN after inserting the sensor readings as evidence was proposed. The proposed methodology has been tested on the data obtained from a TPS simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the TPS

Fault detection and diagnostics of a three-phase separator

A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down. A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator

Quantitative risk prognostics framework based on Petri Net and Bow-Tie models

A simulation framework based on the Petri Net model is proposed in this paper used for performing quantitative risk prognosis through extending the Bow-Tie model. A Petri Net model is built to include features, specific to assets, such as the condition of the asset, the projected operational usage, inspection and maintenance policies and degradation process, so that the future condition of the asset over time can be estimated. Several new Petri Net modelling features which advance the traditional Bow-Tie approach are proposed, such as asset usage generating and usage dependent transitions, and the possibility of entering evidence about the actual condition of the asset through the use of truncated distributions. Monte Carlo simulation method is used to simulate the developed Petri Net model over a selected time frame, in order to obtain statistics necessary to perform risk assessment using the Bow-Tie model. The paper reports on the overall proposed methodology and then focusses on the development of the Petri Net model. The methodology is applied in risk prognostics of operating an underground passenger lift. In particular, the combination of the Petri Net and the Bow-Tie models is illustrated to predict the likelihood and the consequences of an event when a lift gets stuck in a shaft between landings

Bayesian belief networks for fault detection and diagnostics of a three-phase separator

A three-phase separator (TPS) is one of the key components of offshore oil processing facili-ties. Oil is separated from gas, water and solid impurities by the TPS before it can be further processed. Fail-ures of the TPS can lead to unplanned shutdowns and reduction of the efficiency of the whole oil processing facility as well as posing hazards to safety of personnel. A novel fault detection and diagnostic (FDD) meth-odology for the TPS is proposed in this paper. The core of the methodology is based on Bayesian Belief Net-works (BBN). A BBN model is built to replicate the operation of the TPS: when the system is fault free or operating with single or multiple failed components. Results of the capabilities of the BBN model to detect and diagnose single and multiple faults of the TPS components are reported in this paper

A fault detection method for railway point systems

Failures of railway point systems (RPSs) often lead to service delays or hazardous situations. A condition monitoring system can be used by railway infrastructure operators to detect the early signs of the deteriorated condition of RPSs and thereby prevent failures. This paper presents a methodology for early detection of the changes in the measurement of the current drawn by the motor of the point operating equipment (POE) of an RPS, which can be used to warn about a possible failure in the system. The proposed methodology uses the one-class support vector machine classification method with the similarity measure of edit distance with real penalties. The technique has been developed taking into account specific features of the data of infield RPSs and therefore is able to detect the changes in the measurements of the current of the POE with greater accuracy compared with the commonly used threshold-based technique. The data from infield RPSs, which relate to incipient failures of RPSs, were used after the deficiencies in the data labelling were removed using expert knowledge. In addition, possible improvements in the proposed methodology were identified in order for it to be used as an automatic online condition monitoring system

Extended Bow-Tie model for asset risk and reliability modeling: application to a passenger lift

A risk and reliability modelling framework for railway assets based on the Petri Net and the Bow-Tie models is proposed in this paper. A Petri Net model together with the Monte Carlo simulation is used to replicate the projected operational usage of the asset, inspection and maintenance policies and degra-dation of the asset and to estimate the future condition of the asset over time. Statistics obtained from the Petri Net are used as inputs to the Bow-Tie model, which is then used to estimate the risk of a hazardous event. The paper reports on the proposed methodology and the results of a case study of an underground passenger lift. In particular, the likelihood and the consequences of a lift getting stuck in shaft between landings are calculated

Bayesian belief networks for fault detection and diagnostics of a three-phase separator

A three-phase separator (TPS) is one of the key components of offshore oil processing facili-ties. Oil is separated from gas, water and solid impurities by the TPS before it can be further processed. Fail-ures of the TPS can lead to unplanned shutdowns and reduction of the efficiency of the whole oil processing facility as well as posing hazards to safety of personnel. A novel fault detection and diagnostic (FDD) meth-odology for the TPS is proposed in this paper. The core of the methodology is based on Bayesian Belief Net-works (BBN). A BBN model is built to replicate the operation of the TPS: when the system is fault free or operating with single or multiple failed components. Results of the capabilities of the BBN model to detect and diagnose single and multiple faults of the TPS components are reported in this paper