Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

Data fusion for fault diagnosis in smart grid power systems

In smart grid power systems, fast and accurate fault detection and diagnosis (FDD) is vital for isolating faulty components and avoiding further complications. This paper introduces a new data fusion method based on ordered weighted averaging (OWA) operator for power smart grids. For this purpose, the discrete time data from circuit breakers (CB) is combined with continuous time data of recorders to enhance the reliability of the fault diagnosis approach. Radial basis functions (RBF) artificial neural network and wavelet transform (WT) are individually employed to identify the location of the fault from the continuous voltage of the buses. Then, a combination of these two methods along with the information from CBs are utilized into a unique framework by OWA operator to diagnose the faults at an early stage. IEEE standard 14 bus system is used to illustrate and validate the proposed method. Several phase to ground faults are injected into the simulation model to validate the diagnostic capability of the FDD system. Simulation results show a better performance of the fusion FDD system in comparison with three other methods

A control oriented cyber-secure strategy based on multiple sensor fusion

This paper introduces a cyber-secure strategy for radar tracking systems. Two common cyber attacks including denial-of-service (DoS) and false data injection (deception) attacks are investigated. The proposed secure control strategy consists of two subsystems: 1) an attack detection and isolation (ADI) subsystem, and 2) a resilient observer (RO) subsystem. The ADI subsystem is used to observe the state of the system using a bank of Kalman Filters and multi-sensor measurements. Then, residuals generated by local Kalman filters are used to isolate the cyber attacks. Afterward, ordered weighted averaging (OWA) operator is utilized to drive a resilient observer to estimate the real correct value of variables such as position under cyber attacks. Weighting factors of the OWA operator are derived using the covariance matrix, and proof of convergence is provided. Simulation studies on a radar tracking system show that the proposed secure control strategy using multi-sensor fusion enhances the performance of the system and results in a more resilient control system against cyber attacks

SMS-A Security Management System for Steam Turbines Using a Multisensor Array

Cyber-physical systems, such as large power plants, apply open networks for monitoring and control purposes. This may increase the risk of cyberattacks to these infrastructures. Cybersecurity methods have been employed as promising techniques to deal with cyber threats and isolate possible cyberattacks. This article introduces a new security management system (SMS) for an industrial steam turbine. As such, the most probable threats, such as denial-of-service (DoS) attack, deception attack, and replay attack in various sensors and actuators of the steam turbine system, are considered. Then, a new SMS system consisting of an attack detection unit and an attack isolation unit is designed. The attack detection unit utilizes a dynamic neural network to detect any potential attack in the system using the concept of residual generation. The attack isolation unit identifies the type of attacks using an integrated feature selection strategy and support vector machine classifier through multisensor information. Several case studies are investigated to evaluate the proposed SMS. The test results show the effectiveness of the proposed SMS with multisensor information when compared to SMS without multisensor array

A New Hybrid Fault Prognosis Method for MFS Systems Based on Distributed Neural Networks and Recursive Bayesian Algorithm

This article introduces a new hybrid prognosis method to predict a remaining useful lifetime (RUL) of multi-functional spoiler (MFS) systems. The MFS is vital to the healthy operation of aircraft spoiler control systems, and any fault or failure in these systems could compromise the safe operation of the aircraft. The proposed prognosis methodology is a hybrid framework composed of a failure parameter estimation unit and an RUL unit. The failure parameter estimation unit observes the failure parameters using distributed neural networks via available measurements of the MFS system. Simultaneously, the remaining useful life is anticipated by the RUL unit employing the estimated failure parameter with a recursive Bayesian algorithm. Moreover, a relative accuracy (RA) measure is invoked to validate the effectiveness of the proposed method. Simulink model of the MFS system is verified by experimental data of the LJ200 series aircraft under fight condition. Furthermore, simulation test results indicate a high accuracy of the distributed structure compared to a centralized network

Recent Survey of Large-Scale Systems: Architectures, Controller Strategies, and Industrial Applications

Complex, dynamical systems, often of a high order, composed of several interconnected subsystems, are referred to as large-scale systems (LSSs). This article presents a survey of the most common LSS architectures. The article then proceeds to discuss conventional control schemes, including decentralized, distributed, and hierarchical structures for these LSSs. Finally, some relevant and recent application domains such as power systems, transportation systems, and industrial processes are outlined. The article concludes by outlining some possible future research and development directions

Harmonic Fault Diagnosis in Power Quality System Using Harmonic Wavelet

The increasing use of non-linear loads such as power electronics, converters, arc furnaces, transformers, fluorescent and high intensity discharge lights have caused harmonics distortion in power quality (PQ) systems. On the other hand, harmonics have numerous effects on electrical systems. For examples, they can be troublesome to communication systems, they increase heating in the transformers and motors, and consequently decrease their life cycle. The first step to address these issues is to diagnose harmonic faults in power distribution systems. This paper introduces a new method for detecting harmonic faults using harmonic wavelets. For this purpose, harmonic wavelet transform (HWT) is used to decompose the faulty signal at different levels. Then, the energies of the decomposition levels based on parseval\u27s theorem are computed. Finally, the faulty signal is reconstructed with harmonics wavelets. Simulation results show that the suggested fault detection and diagnosis (FDD) system can successfully identify the maximum harmonic in the faulty signal and the amount of harmonics in the faulty signal compared to fundamental signal

A distributed fault detection and isolation method for multifunctional spoiler system

The increasing complexity of aircraft subsystems and control structure invoke new fault diagnosis methodologies for these vehicles. Multifunctional spoiler (MFS) is an essential part of an aircraft spoiler control system that can be easily deteriorated due to faults which could consequently compromise the safety of the aircraft. The MFS consists of several components with highly nonlinear dynamics. This paper presents a new fault detection and isolation (FDI) system using dynamic neural networks (DNN) to deal with incipient faults at their early stages. For this purpose, an intelligent distributed FDI framework consisting of three DNNs is employed for generating residual set in the system to observe any discrepancy in the states of the system. Furthermore, the dynamic structure of the designed neural networks helps the observers tackle the non-linearity of the system and provides the fault isolation in the whole operating range. Simulation results are conducted to demonstrate the ability and effectiveness of the proposed FDI system

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems with Missing Measurements

A new fusion strategy is introduced in this article to estimate state for multi-rate multi-sensor systems with missing measurements. N sensors, which possess various sampling rates, render the measurements. Missing measurements with a certain probability pattern are also investigated. For these types of systems, Multi-rate Kalman filters are designed to estimate a target position at various sampling rates. Next, Ordered Weighted Averaging (OWA) operator is utilized to integrate multi-rate Kalman filters and improve the estimation quality. A new fusion strategy based on a real covariance matrix is introduced for updating the weighting factors, and proof of convergence is granted. Simulation studies on a tracking system verify the superior performance of the proposed fusion strategy in comparison with the Kalman filter, the multi-rate Kalman filters, and also the previous fusion methodology

Enhanced COVID-19 Detection by chest x-ray images using transfer learning-based extracted deep features and information fusion

One of the essential factors to limit the spreading of COVID-19 is an early and accurate diagnosis. Chest X-rays (CXRs) imaging is a common approach to identify COVID19, owing to its ability to detect the respiratory problem as a major symptom of COVID-19 and its public access even in third-world countries. A robust and efficient classification by an intelligent computer-aided model plays a prominent role in facilitating this procedure. In this work, a fusion strategy using Transfer Learning (TL) on a Deep Convolutional Neural Network (DCNN), optimized Ensemble Decision Tree (EDT) and Support Vector Machine (SVM) is introduced to classify the positive and negative COVID-19 cases through using Chest X-rays (CXRs) images. First, a ResNet50 approach is applied to perform a direct classification and to extract deep features. Next, Principal Component Analysis (PCA) is employed on the extracted deep features from the ResNet50 to establish new reduced and uncorrelated feature space. Then, these features are forwarded to SVM and EDT for classification. Hyperparameters of SVM and EDT are optimized by Bayesian Optimization (BO) algorithm. In the last step, Majority Voting (MV) is employed to integrate the classification results and identify COVID19. The main benefit of the proposed COVID19 detection scheme is that the deep features automatically capture COVID19 patterns and improve detection efficiency. In addition, the integrated information from various optimized approaches enhances the classification accuracy and leads to more robust and reliable results