Fault Detection And Identification System Research Articles

Application of interval type-2 fuzzy logic systems to gas turbine fault diagnosis

Several approaches have been employed for gas turbine Fault Detection and Identification (FDI), since a reliable FDI system with minimum false alarm rate can effectively reduce maintenance cost and downtime. This paper introduces the application of Interval Type-2 Fuzzy Logic Systems (IT2FLSs) to gas turbine fault diagnosis for the first time. The proposed FDI system is composed of a bank of IT2FLSs, trained for state detection and health assessment of an industrial gas turbine at various operating conditions. For this purpose, train and test data are generated by applying mechanical fault signatures to gas turbine’s mathematical model. Fuzzy Rule Base is then developed by means of Interval Type-2 Fuzzy C-Means (IT2FCM) clustering, and parameters of the IT2FLSs are optimized using a metaheuristic algorithm. Finally, the performance of the IT2FL based FDI system is compared to several classification techniques. It is concluded that as a compromise among the objectives of online applicability, accuracy, reliability against measurement uncertainty, incipient fault diagnosis, robustness against abrupt sensor failure and generalization capacity, the proposed method demonstrates a promising performance.

Active Fault-Tolerant Control With Imperfect Fault Detection Information: Applications to UAVs

A major issue in active fault-tolerant control (FTC) is having to rely on uncertain information provided by the fault detection and identification (FDI) algorithm. To achieve a reliable FTC, the controller needs to be robust against FDI uncertainties, namely missed “small” faults and fault detection delays. In this article, the FDI system only needs to indicate which actuator is faulty after a predefined amount of time, but does not need to estimate the actuator fault value or its faulty position. The goal is to design a FTC, which is robust against potentially undetected “small” actuator faults and which guarantees boundedness of the unstable poles for all possible actuator fault types. An adapted μ analysis is developed to analyze the unstable system poles, due to the presence of a fault. It is integrated within a DK-iteration approach to synthesize a controller in a robust control framework with H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -design objectives. The synthesis is shown on a fixed-wing UAV, where fault-tolerance against single-actuator faults is achieved. Real flight experiments of a UAV with aileron and flap faults show the validity of the approach.

A novel gas turbine fault detection and identification strategy based on hybrid dimensionality reduction and uncertain rule-based fuzzy logic

Persistent health monitoring of a gas turbine engine preserves the operational efficiency and lifetime of its components. An online monitoring system should meet requirements such as high accuracy, reliability against measurement uncertainty, and ease of implementation. Factors such as high dimensions of data, variety of deteriorations, and similarity of fault patterns are also challenging to the Fault Detection and Identification (FDI) process. In this paper, a new gas turbine health assessment strategy based on hybrid dimensionality reduction and Interval Type-2 Fuzzy Logic (IT2FL) is developed, and it is demonstrated that the proposed pre-processing method not only reduces the computational burden, but also preserves the part of information that is necessary for maintaining the reliability of the FDI procedure. In this approach, raw data are projected to a nonlinear self-organized feature space, and optimum feature subsets are selected using multi-objective optimization. Pre-processed data are then utilized for training a bank of IT2FL Systems (IT2FLSs), which are developed to estimate the gas turbine’s health status at various ambient and working conditions. The proficiency of the proposed approach is compared to other feature extraction methods, and it is indicated that the Self-Organizing Map (SOM) based FDI system outstands in terms of incipient fault diagnosis, reliability, and robustness against measurement uncertainty.

Model-Based Fault Diagnosis of an Anti-Lock Braking System via Structural Analysis

The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-determined (MSO) sets selection, and MSO-based residual design. In terms of the four faults in the ABS, they are evaluated to be detectable through performing a structural representation and making the Dulmage-Mendelsohn decomposition with respect to the fault modelling, and then they are proved to be isolable based on the fault isolability matrix via SA. After that, four corresponding residuals are generated directly by a series of suggested equation combinations resulting from four MSO sets. The results generated by numerical simulations show that the proposed FDI system can detect and isolate all the injected faults, which is consistent with the theoretical analysis by SA, and also eventually validated by experimental testing on the vehicle (EcoCAR2) ABS.

Design and Evaluation of a Structural Analysis-Based Fault Detection and Identification Scheme for a Hydraulic Torque Converter.

A hydraulic torque converter (HTC) is a key component in an automatic transmission. To monitor its operating status and to detect and locate faults, and considering the high-efficiency fault detection and identification (FDI) scheme design by the methodology of structural analysis (SA), this paper presents an SA-based FDI system design and validation for the HTC. By the technique of fault mode and effect analysis (FMEA), eight critical faults are obtained, and then two fault variables are chosen to delegate them. Fault detectability and isolability, coupled with different sensor placements, are analyzed, and as a result, two speed sensors and two torque sensors of pump and turbine are selected to realize the maximal fault detectability and fault isolability: all six faults are detectable, four faults are uniquely isolable, and two faults are isolated from the other faults, but not from each other. Then five minimal structurally overdetermined (MSO) sets are easily acquired by SA to generate five corresponding residuals. The proposed FDI scheme of the HTC by SA is first validated by a theoretical model, then by an offline experiment in a commercial SUV, and the testing results indicate a consistent conclusion with the simulations and theory analysis.

Active Fault Tolerant Control with self-enrichment capability for gas turbine engines

In this paper, an Active Fault Tolerant Control (AFTC) system with self-enrichment capability is proposed to deal with both the anticipated and unanticipated gas-path performance deteriorations in the industrial gas turbine engines. For this purpose, a hybrid fault diagnosis system with self-enrichment capability is proposed to take advantage of the positive features of fuzzy-based and global optimization-based Fault Detection and Identification (FDI) methods. The proposed FDI system provides the possibility of dealing with the unanticipated or unknown deteriorations. In order to design the proposed AFTC system, the parameters of the control system are optimized for a set of predefined deteriorations and through this a bank of control system parameters is created for the anticipated health conditions. The proposed FDI system verifies whether the detected health condition is an anticipated condition or not. In the case of the anticipated deteriorations, the proposed AFTC system selects the controller parameters from the bank of control system parameters, according to the current health condition; while in the case of the unanticipated deteriorations, the controller parameters will be determined through the optimization process. By adding the new set of optimized parameters to the bank of control system parameters, the database of the proposed AFTC system will gradually enrich to cope with a broader range of the engine deteriorations. The results obtained in the present work reveal that, the proposed AFTC system can preserve the engine safety and operational constraints; in the case of some deteriorations at the expense of a drop in the availability (due to a necessary trip or load shedding) and in some other cases even without losing the engine availability (by avoiding any unnecessary trip or load shedding). This is while, according to the results obtained, in the case of some health conditions, employing the controller designed for clean condition may cause the turbine over-temperature, loss of surge margin, and also a drop in the availability.

A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration

Considering the fact that the signs of performance deterioration in the main components of gas turbines vary in different operating points, thus designing a Fault Detection and Identification (FDI) system using a single fault pattern for all engine operating ranges can reduce the ability of the diagnosis system to identify the intensity or even the type of any potential degradation. So, in this article, with the aid of the “load” parameter as an augmented input and using the fault patterns obtained at different part-load conditions, a fuzzy-based FDI system is proposed for an industrial two-shaft gas turbine with the ability to use in both the full and part-load operations. In the proposed FDI system, fuzzy rule base is generated by a table look-up scheme and by employing the available input/output data extracted from fault signature table. Moreover, a global optimization technique is used to determine some database parameters, such as the number of membership functions of input variables and their standard deviations. The optimization is carried out to make a compromise between the diagnosis accuracy and robustness against measurement noise. In the present work, the performance of the proposed FDI system is evaluated against the most common cause of gas turbine performance deterioration i.e. fouling and erosion in terms of the success rate and the estimation accuracy at different levels of sensor noise. The results obtained indicate that the proposed FDI system can considerably reduce the average estimation error by 0.1–0.75% and increase the success rate by 10–20% compared with the diagnosis systems designed for a specific operating point. The results also demonstrate that the proposed FDI system has robust performance against measurement uncertainties, and moreover smearing effects are rarely seen in the results.

Building a robust integrity monitoring algorithm for a low cost GPS-aided-INS system

In this paper, the integrity of low cost GPS/INS systems is investigated to ensure the ability to obtain continuous high-integrity, high-accuracy vehicle state estimate under low-computational system requirement. The utilization of two fault detection and identification (FDI) techniques, the χ2 (or sometimes referred to as chi-squared) gating function and the multiple model adaptive estimation (MMAE), is proposed to monitor the integrity of GPS measurements. A fault in GPS measurements is modeled with an increase in GPS measurements noise covariance matrix which may result from mistuning of filter’s noise parameters, interference, jamming, or multipath errors. These types of faults are covered by this work and are assumed to last for unconstrained period of time. ξ2 FDI systems are computationally very inexpensive, have good fault detection ability and require no a priori knowledge on system dynamics. However, they are sensitive to filter tuning and fail to detect faults when the filter converges to them rather than rejecting them. Model-based approaches provide outstanding FDI ability. However, they are computationally demanding, require a priori knowledge on system model, sensitive to mismodeling errors, have finite convergence time, and compromise filter optimality under no-failure conditions. The proposed fusion algorithm guarantees integrity and does not affect filter’s optimality under no-failure conditions. Simulated and experimental tests were conducted to verify the accuracy of the proposed techniques. Results are presented at the end of the paper to highlight the performance characteristics of the proposed FDI system implementation.

Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

Robust Dead-Reckoning System with Function on Sensor-Fault Diagnosis for Mobile Robot.

An approach to sensor fault detection and identification (FDI) in the dead reckoning of mobile robots is proposed. It is based on the interacting multiple-model (IMM) algorithm. Changes of sensor normal/failure modes are explicitly modeled as switching from one mode to another in a probabilistic manner, and mode probabilities and estimates from single-model-based filters, each of which is based on a model matching to a particular system mode, interact with each other effectively. To provide better fault decision, a simple decision rule is incorporated into the IMM based algorithm. The proposed FDI algorithm is implemented on our mobile robot. Sixteen system modes (one normal mode and fifteen 'hard' sensor-failure modes) of four internal sensors (two wheel-encoders, a steering potentiometer and a yaw-rate gyro) are handled. Experimental results illustrate that the dead reckoning with the proposed FDI system allows robust motions of the robot subject to sensor failures.

Incipient Fault Detection and Identification in Process Systems Using Accelerated Neural Network Learning

The objective of this paper is to present the development and numerical testing of a robust fault detection and identification (FDI) system using artificial neural networks (ANNs), for incipient (slowly developing) faults occurring in process systems. The challenge in using ANNs in FDI systems arises because of one's desire to detect faults of varying severity, faults from noisy sensors, and multiple simultaneous faults. To address these issues, it becomes essential to have a learning algorithm that ensures quick convergence to a high level of accuracy. A recently developed accelerated learning algorithm, namely a form of an adaptive back propagation (ABP) algorithm, is used for this purpose. The ABP algorithm is used for the development of an FDI system for a process composed of a direct current motor, a centrifugal pump, and the associated piping system. Simulation studies indicate that the FDI system has significantly high sensitivity to incipient fault severity, while exhibiting insensitivity to sensor noise. For multiple simultaneous faults, the FDI system detects the fault with the predominant signature. The major limitation of the developed FDI system is encountered when it is subjected to simultaneous faults with similar signatures. During such faults, the inherent limitation of pattern-recognition-based FDI methodsmore » becomes apparent. Thus, alternate, more sophisticated FDI methods become necessary to address such problems. Even though the effectiveness of pattern-recognition-based FDI methods using ANNs has been demonstrated, further testing using real-world data is necessary.« less