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

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.

The vehicle lateral control system performs lane-keeping and lane-changing tasks on automated highways. The controller acquires the vehicle's lateral positions through two sets of magnetometers. Failures on either set of magnetometers may cause instability of the system and jeopardize safety of the passengers. Hence it is of crucial importance to design and implement a fault detection and identification (FDI) mechanism which is responsible for handling the magnetometers' malfunctions. In this paper, we propose an FDI scheme consisting of two switching observers and a probabilistic inference algorithm. The existence of failures is detected by evaluating the posterior probabilities of fault occurrences, while the sizes and types of the faults are estimated by the observers. Simulation results show that sensor failures can be detected and identified in a timely manner. Moreover the lateral control system maintains stability, and an acceptable level of performance is achieved in the presence of sensor failures.

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

In this paper, a fault detection, identification and estimation approach has been developed for the condition monitoring of the electro-hydraulic actuator (EHA) system using the multiple-model (MM) estimation algorithm. The MM estimation algorithm makes use of the extended Kalman filter (EKF) technique to generate estimates of states and key physical parameters, which are related to faults in the EHA system. The proposed fault detection and identification (FDI) is formulated as a hybrid interacting multiple-model estimation scheme. The interaction scheme between multiple models is introduced into the MM estimation algorithm to yield more robust detection and estimation. Estimates of the key physical parameters in the EHA system are assessed against baseline values and fused with the FDI results for higher level monitoring purposes. Two parameters of interests, namely torque motor equivalent resistance and the effective bulk modulus are investigated for the EHA system condition monitoring purpose. The simulation results highlight the considerable potential of the proposed technique for achieving improved condition monitoring of the EHA system.

Fault Detection and Identification Using Neural Network and Fuzzy Logic

This paper considers the problem of fault detection and identification (FDI) in applications carried out by a group of unmanned aerial vehicles (UAVs) with visual cameras. In many cases, the UAVs have cameras mounted onboard for other applications, and these cameras can be used as bearing-only sensors to estimate the relative orientation of another UAV. The idea is to exploit the redundant information provided by these sensors onboard each of the UAVs to increase safety and reliability, detecting faults on UAV internal sensors that cannot be detected by the UAVs themselves. Fault detection is based on the generation of residuals which compare the expected position of a UAV, considered as target, with the measurements taken by one or more UAVs acting as observers that are tracking the target UAV with their cameras. Depending on the available number of observers and the way they are used, a set of strategies and policies for fault detection are defined. When the target UAV is being visually tracked by two or more observers, it is possible to obtain an estimation of its 3D position that could replace damaged sensors. Accuracy and reliability of this vision-based cooperative virtual sensor (CVS) have been evaluated experimentally in a multivehicle indoor testbed with quadrotors, injecting faults on data to validate the proposed fault detection methods.

In the modern industry, electro-hydraulic actuators (EHAs) have been applied to various applications for precise position pressure/ force control tasks. However, operating EHAs under sensor faults is one of the critical challenges for the control engineers. For its enormous nonlinear characteristics, sensor fault could lead the catastrophic failure to the overall system or even put human life in danger. Thus in this paper, a study on mathematical modeling and fault tolerant control (FTC) of a typical EHA for tracking control under sensor-fault conditions has been carried out. In the proposed FTC system, the extended Kalman-Bucy unknown input observer (EKBUIO) -based robust sensor fault detection and identification (FDI) module estimates the system states and the time domain fault information. Once a fault is detected, the controller feedback is switched from the faulty sensor to the estimated output from the EKBUIO owing to mask the sensor fault swiftly and retains the system stability. Additionally, considering the tracking accuracy of the EHA system, an efficient brain emotional learning based intelligent controller (BELBIC) is suggested as the main control unit. Effectiveness of the proposed FTC architecture has been investigated by experimenting on a test bed using an EHA in sensor failure conditions.

This paper presents an approach to classifying power system faults using rough set methods. A knowledge-based fault detection and identification (FDI) system for power system faults has been introduced. The FDI system has the ability to detect and classify power system faults by combining conventional signal analysis methods (e.g., FFT, IFFT and wavelets) with granular computing and rough set methods. In granular computing, experimental data is partitioned into collections of data (called information granules) that are in some way similar. Rough set methods are based on set approximation, partition of each finite universe using an indiscernibility relation, attribute reduction, decision-rule derivation, and many useful measures such as approximation accuracy and rough inclusion. Traditional fuzzy set theory is also as part of fault signal feature extraction. The FDI system derives an indication of the type of faults that have occurred and also generates classification rules for the fault classification. This system has resulted from a study of fault files recorded by the Transcan Recording System (TRS) at the Manitoba Hydro Dorsey Station over several years. The contribution of this paper is the introduction of an approach to classifying power system faults using a combination of traditional signal analysis methods and a number of computational intelligence methods (granular computing, and rough set theory).

Electro Hydraulic Actuators (EHAs) keep their role as the leading solution for the control of current generation primary flight control systems: the main reason can be found in their high power to weight ratio, much better than other comparable technologies. To enhance efficiency and reliability of modern EHAs, it is possible to leverage the diagnostics and prognostics disciplines; these two tools allow reducing life cycle costs without losing reliability, and provide the bases for health management of integrated systems, in compliance with regulations. This paper is focused on the development of a fault detection algorithm able to identify the early signs of EHA faults, through the recognition of their precursors and related degradation patterns. Our methodology provides the advantage of anticipating incoming failures, triggering proper alerts for the maintenance team to schedule adequate corrective actions, such as the replacement of the degraded component. A new EHA model-based fault detection and identification (FDI) method is proposed; it is based on deterministic and heuristic solvers able to converge to the actual state of wear of the tested actuator. Three different progressive failure modes were chosen as test cases for the proposed FDI strategy: clogging of the first stage of the flapper-nozzle valve, spool-sleeve friction increase, and jack-cylinder friction increase. A dedicated simulation model was created for the purpose. The results highlighted that the method is adequate in robustness, since EHA malfunctions were identified with a low occurrence of false alarms or missed failures.

This paper proposes a new principal component analysis (PCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method which maximizes the model sensitivity to a certain process fault type is proposed. This method uses normal operating or known faulty data to build the PCA model and other faulty data to fix its structure. A new structuration method is proposed to identify the process fault. This method computes the common angles between the residual subspaces of the different modes. It generates a reduced set of detection indices that are sensitive to certain process faults and insensitive to others. The proposed FDI scheme is successfully applied to the Tenessee Eastman process (TEP) supposedly affected by several process faults.

This paper describes an interrogation based fault detection and identification (FDI) scheme for a class of large scale systems which are linear time-invariant. The formulation is suitable for implementation on digital signal processors which can operate in a real-time environment. Furthermore, a data compression scheme is presented which reduces the total amount of data to be stored and processed from (n/sup 3/+n/sup 2/) to (n/sup 2/+n).

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

Machine learning (ML) revolutionized traditional machine fault detection and identification (FDI), as complex-structured models with well-designed unsupervised learning strategies can detect abnormal patterns from abundant data, which significantly reduces the total cost of ownership. However, their opaqueness raised human concern and intrigued the eXplainable artificial intelligence (XAI) concept. Furthermore, the development of ML-based FDI models can be improved fundamentally with machine learning operations (MLOps) guidelines, enhancing reproducibility and operational quality. This study proposes a framework for the continuous development of ML-based FDI solutions, which contains a general structure to simultaneously visualize and check the performance of the ML model while directing the resource-efficient development process. A use case is conducted on sensor data of a hydraulic system with a simple long short-term memory (LSTM) network. Proposed XAI principles and tools supported the model engineering and monitoring, while additional system optimization can be made regarding input data preparation, feature selection, and model usage. Suggested MLOps principles help developers create a minimum viable solution and involve it in a continuous improvement loop. The promising result motivates further adoption of XAI and MLOps while endorsing the generalization of modern ML-based FDI applications with the HITL concept.

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

This paper addresses the design of a fault detection and reconfigurable control structure for bimodal piecewise affine (PWA) systems. The PWA bimodal system will be designed to verify input-to-state stability (ISS) in closed loop. The proposed methodology is divided into two parts. First, a Luenberger-based observer structure is proposed to solve the fault detection and identification (FDI) problem for bimodal PWA systems. The unknown value of the fault parameter is estimated by an observer equation, which is derived using a Lyapunov-based methodology. Then, the ISS property is proved for the observer. Second, a fault-tolerant state feedback controller is synthesized for the PWA model. The controller is designed to deal with partial loss of control authority identified by the observer. The ISS property is also proved for the controller. Finally, the ISS property for the interconnection of the controller and the observer-based fault identification mechanism is studied. The design procedure is formulated as a set of linear matrix inequalities (LMIs), which can be solved efficiently using available software packages.