Fault Detection And Isolation Technique Research Articles

An energy graph eigendecomposition approach to fault detection and isolation applied to a gas-to-liquids process

Fault detection and isolation (FDI), which make up a large part of a process monitoring protocol, is a refined scheme which aims to detect and isolate anomalies that occur within an industrial plant. For the past 50+ years, much work has been done on developing FDI schemes for a vast array of different applications. In recent years, novel energy-based FDI techniques were proposed, as energy is seen as a unifying parameter of different domains. Additionally, the proposed energy-based approaches attempt to capture causal (or structural) information of the considered physical system. Keeping with this theme, this study will determine, after some alterations, the applicability and performance of some of the previously proposed energy-based approaches, especially compared to one another, when applied to a single, larger-scale gas-to-liquid (GTL) process. The approaches covered in this study include one qualitative eigendecomposition approach, one quantitative eigendecomposition approach, and a graph matching approach utilising a distance parameter (DC-value). Even though the quantitative eigendecomposition approach revealed improved detection sensitivity over the DC-value approach for faulty conditions, both techniques revealed a detection accuracy of 89%. The DC-value approach could correctly isolate 71% of the fault cases while the quantitative and qualitative eigendecomposition approaches could respectively only match 37.69% and 11% correctly. Even though the eigendecomposition approaches could not outperform the DC-value approach, the resolution benefit of 6 parameters that it offers warrants further research.

Energy-based fault detection and isolation of a Brayton cycle-based HTGR power conversion unit – A comparative study

The amount of data associated with condition monitoring of large scale industrial plants has led to a “big data problem”. Energy-based fault detection and isolation (FDI) has been proposed as a hybrid FDI method to reduce data requirements and improve FDI performance. In this paper, a comparative study is performed on three energy-based FDI techniques, namely: an enthalpy–entropy error-based technique, a residual-based technique and an eigendecomposition-based technique. A representative Brayton cycle-based power conversion unit (PCU) is considered as the case study and simulated under normal and fault conditions. From the simulation data, the PCU is represented in terms of energy, either as an enthalpy–entropy diagram or an attributed graph. The graph representation allows structural information to be captured which contributes towards fault isolation capability. For each FDI technique, operational energy signatures are compared to normal and fault reference energy signatures to detect and isolate fault conditions. This paper contributes by comparing the FDI techniques based on the attributes of quick detection and isolation. The residual-based technique had the best performance in detecting and isolating faults. The results also indicate that a combination of techniques will result in an FDI technique with improved detection and isolation performance over the individual techniques.

Switch fault diagnosis for boost DC–DC converters in photovoltaic MPPT systems by using high‐gain observers

Open- and short-circuit faults (OSCFs) in boost dc–dc converters for photovoltaic (PV) maximum power point trackers (MPPTs) imply an inefficiency after fault is triggered, which affect the security and profitability of PV projects. Hence, fault detection and isolation (FDI) techniques have become an important issue for PV technology. In this study, a model-based FDI technique is proposed to boost dc–dc converters in PV MPPT systems. As is well-known, major issues of model-based FDI techniques have always been parametric uncertainty and no-modelled dynamics. This study focuses on how to mitigate these shortcomings by applying a high-gain observer (HGO) as a residual generator. A striking feature of HGO's is that exponential stability is still guaranteed for bounded disturbances (or faults). As demonstrated in this study, under an integral control action in the closed-loop control system, OSCFs are characterised for ever-growing signals, enabling the suggested FDI scheme. Also, the FDI proposal is decoupled from PV current (irradiance changes) and load variations, thereby avoiding false alarms. Moreover, the output-injection gain and thresholds are selected such that the fault diagnosis is achieved in eight switching cycles, enabling a fast and reliable diagnosis. Experimental results are illustrated to validate the FDI scheme proposed in this study.

Online Fault Detection and Isolation in Advanced Heavy Water Reactor Using Multiscale Principal Component Analysis

Signals from sensors placed at different locations are used for control and protection purposes in a nuclear reactor. It also requires in-service fault detection and isolation (FDI) for its safe operation. The sensor signals are generally a superimposition of low-frequency components representative of true values of the variables being monitored, occasional high-frequency periodical oscillations due to disturbances and faults, and sensor faults. Techniques like principal component analysis (PCA) can be used for FDI; however, it would be more meaningful if the FDI technique can also help for predictive maintenance of reactor internals through vibration spectra. To address these issues, a multiscale PCA, integrating wavelet transform with PCA and aiming to reduce the modeling cost by using only a fewer scales that contribute to the monitoring, has been proposed in this paper for online FDI of an advanced heavy water reactor (AHWR). A new mathematical formulation of the generalized likelihood ratio test for its use with wavelet approximation coefficients has also been proposed for better sensor-FDI outcomes. The proposed approach detects and isolates sensor faults and process faults using the signals from neutron detectors. Efficacy of the proposed technique is established on the simulated ex-core ion chamber data of AHWR considering different scenarios that involve localized frequency contents representative of process faults, slowly developing (incipient) sensor faults, and the simultaneous presence of two or more of these scenarios. Simulation results validate the effectiveness of the proposed scheme for online FDI in the reactor.

Application of Exergy-Based Fault Detection in a Gas-To-Liquids Process Plant.

Fault detection and isolation (FDI) within the petrochemical industries (PCIs) is largely dominated by statistical techniques. Although a signal-based technique centered on exergy flows within a process plant was proposed, it has only been applied to single process units. The exergy-based scheme has not yet been applied to process plants that feature at least a single recycle stream. The Tennessee Eastman process (TEP) is commonly used as an FDI benchmark process, but due to obfuscation, the TEP cannot be directly implemented in a commercial process simulator. Thus, application of FDI techniques to proprietary processes will require significant investment into the implementation of the FDI scheme. This is a key impediment to the wide-spread comparison of various FDI techniques to non-benchmark processes. In this paper, a gas-to-liquids (GTL) process model is developed in Aspen HYSYS®, and the model’s performance is validated. The exergy-based FDI technique is applied to the GTL process while the process is subjected to carefully selected faults. The selected faults aim to affect several process units, and specifically, the resultant recycle stream of the GTL process is considered. The results indicate that even though the exergy-based technique makes use of fixed thresholds, complete detection and isolation can be achieved for a list of common process faults. This is significant since it shows, for the first time, that the exergy-based FDI scheme can successfully be deployed in processes with recycle streams.

Computational cost of an exergy-based fault detection scheme implemented in a commercial process simulator

Abstract Energy-based fault detection and isolation (FDI) schemes show promise in terms of applicability to petrochemical industries and the broader process industries. Model-based FDI schemes offer pronounced advantages over more easily implemented data-driven FDI techniques. However, the implementation of model-based FDI schemes require the development of analytic models, and as such, widespread adoption is lacking. Although process simulators derive analytic models internally, comprehensive exergy-based parameters are not calculated automatically. In this work Aspen Hysys is extended by means of user variables to allow the automatic calculation of physical and chemical exergy for each process stream. Initial results indicate that additional computational overhead of approximately 20% is incurred and that the calculation technique scales well with model size. This calculation method allows the development of a truly hybrid model- and data-based approach to FDI within the process industries.

Optimal Combination of Fault Detection and Isolation Methods of Integrated Navigation Algorithm for UAV

Performing endurance flight for long-term missions, unmanned aerial vehicles (UAV) are safety–critical systems that requires high reliability. To achieve long flight time and high reliability altogether, S/W-based fault detection and isolation (FDI) methods are applied rather than H/W redundancy due to limitation of loading weight. This study focused on model-based FDI techniques against faults, difficult to sense via signal-based FDI methods. The existing observer-based and parameter identification-based methods have difficulties separating the faulty sensor from correlated fault sources or sensors that output the same type of measurements. In this study, the fault detection parameter was designed to detect a faulty sensor from correlated fault sources. The combination of the proposed method and the existing residual-based methods was also designed to separate the faulty sensor from other independent auxiliary sensors with same measurement variables. In addition, the optimal combination of the CUSUM test and technique using the fault detection parameter was designed for UAVs. The algorithm was installed on INS and verified via vehicle mounted test and captive flight test.

Analysis of transient data in test designs for active fault detection and identification

Active model-based Fault Detection and Isolation (FDI) methods are of increasing importance in modern cyber-physical systems due to their ability to generate detections as residuals between anticipated and observed information, and, thus, isolate and identify causes of faults with greater confidence than conventional passive FDI techniques. This work focuses on evaluating the effectiveness of active FDI tests which use either steady-state or dynamic information. It is shown that transient information from active FDI tests can improve the identifiability of faults compared to steady-state testing. These tests are designed by casting FDI as an optimization problem that maximizes the Fisher Information Matrix of a system sensed outputs with respect to faults. The identifiability of faults is examined at steady-state and transient FDI tests in a plate fin heat exchanger of an aircraft environmental control system (ECS). In this system particulate fouling needs to be detected, which is challenged by multiple sources of system uncertainty. It is shown that the inclusion of transient information during fault diagnosis increases the confidence in fault identification when using optimal test designs.

Robust Power-Electronic-Converter Fault Detection and Isolation Technique for DFIG Wind Turbines

This paper proposes a robust fault detection and isolation (FDI) technique for the power electronic converter (PEC) of doubly-fed induction generator (DFIG) wind turbines (WTs), and in particular for open-circuit faults herein. It combines fault indicators based on the processing of the Clarke transformation of the converter currents and a statistical change detection algorithm, namely a cumulative sum (CUSUM) algorithm that detects significant changes in the variance of the reactive power. This allows for a reduction of the false alarm rate compared to an approach relying exclusively on the current analysis. The proposed FDI technique is validated by means of both simulation and experimental results.

Application of data reconciliation for fault detection and isolation of in-core self-powered neutron detectors using iterative principal component test

In-core neutron detectors are used in large nuclear reactors for the purpose of acquiring knowledge about the spatial flux distribution, which helps in control, monitoring and protection of the reactors. Random errors and faults, which cause degradations of the signals from the true values, invalidate the in-core detector measurement data. A scheme utilizing the analytical redundancy among physically redundant in-core detectors greatly facilitates removing the ill-effects of these errors. Particularly, data-based or process-history-based schemes are suitable when there are a large number of variables as in the case of in-core detectors. In this paper, application of a Data Reconciliation (DR) scheme (with the aim of minimizing the effect of random errors) coupled with a Fault Detection and Isolation (FDI) technique (which makes the data free from faults) to the in-core detector measurement data of the Advanced Heavy Water Reactor (AHWR) is presented. This reactor uses Vanadium Self-Powered Neutron Detectors (VSPNDs) as in-core neutron detectors. Since the constraint model developed for all the in-core neutron detectors as a whole cannot yield satisfactory performance from the DR and FDI, they are developed for the individual clusters of VSPNDs using the Iterative Principal Component Analysis (IPCA), a multi-variate statistical technique. The FDI technique used is the Iterative Principal Component Test (IPCT) discussed in Sagar et al. (2015c). The effectiveness of this scheme is tested as follows: (i) the mathematical model of the AHWR is used to simulate measurement data from VSPNDs; (ii) the data from VSPNDs are grouped as clusters based on correlations among the variables; and (iii) the effectiveness of the DR-based FDI of VSPNDs in some clusters is established based on some performance indices, when the spatial control of the reactor is active.

Euresis-filter: a robust multiple-model FDI technique for small satellites' thruster faults

Fault detection and isolation (FDI) techniques are properties of critical importance for any aerospace missions where autonomy and fault identification (malfunction diagnosis) must be present all the time. Current work provides an extended presentation of the Euresis-filter, a multiple model-based FDI technique suitable for small satellites' thruster faults. The main scope of the proposed multiple model-based FDI function is to maintain the spacecraft (S/C) operational and safe (healthy status) as required by the mission, including the analysis, development and design of the Euresis-filter to the LPF model and Monte Carlo simulation results related to thruster faults. Results reveal that the Euresis-filter can serve as an advanced FDI and fault tolerance control (FTC) technique in terms of reliability, safety and mission success when compared to classical FDI/FDIR approaches.

Outlier detection by using fault detection and isolation techniques in geodetic networks

Fault detection and isolation (FDI) techniques, which are called standard parity space approach (SPSA) and optimal parity vector approach (OPVA), have been presented in literature extensively for engineering sensor systems or sensor networks. This paper demonstrates the abilities of these approaches to detect and isolate outliers in geodetic networks. The ability to detect and isolate outliers has been measured by computing the mean success rate (MSR) for some given probability of significance levels. These approaches have been applied to a levelling network and a Global Navigation Satellite System (GNSS) network. Different matrix decomposition techniques have been used as an alternative way to the Potter algorithm, which is used in SPSA and OPVA. It has been proven that the abilities of FDI techniques, i.e. the MSRs of OPVA, increase with regard to the ones of SPSA in the levelling network and the GNSS network especially if the significance level α is chosen as 0.001 by using Monte–Carlo simulation.

Fault diagnosis of Pakistan Research Reactor-2 with data-driven techniques

In nuclear reactors, safety is of prime importance in their operation. Fault detection and isolation (FDI) methods are making their applications to improve safety, reliability and availability of nuclear reactors. Among various FDI techniques, data-driven techniques are best suited for fault diagnosis of nuclear reactors because process data is available to sensors, both in normal operation and under faulty conditions. Among data-driven techniques, principle component analysis (PCA) and Fisher discriminant analysis (FDA) have been successfully applied to many industrial processes. In this paper, PCA and FDA are applied for fault detection and fault isolation in Pakistan Research Reactor-2 (PARR-2) for known faults of control rod withdrawal and external reactivity insertion. PCA model is developed using training data set obtained during normal operation of PARR-2. It is then applied to test data set collected from the reactor during control rod withdrawal fault and external reactivity insertion fault. Likewise, FDA model is constructed for the above mentioned faults using the training data set and applied to the test data for fault isolation. The results demonstrate that PCA is successful in detection of both the faults. Additionally, FDA not only detects faults, but it is also successful in isolation/localization of the two faults in PARR-2.

Euphoria-filter: a robust H<SUB align="right">2/H<SUB align="right">∞ technique amenable to the Lisa-Pathfinder thruster faults

The main purpose of the fault detection and isolation (FDI) function is to maintain the spacecraft (S/C) or any other unnamed aerial vehicle (UAVs) operational and safe (healthy) as required by the mission. Thus, robust FDI is the key property for all aerospace missions and applications, wherever autonomy is of paramount importance. Current work provides a focused insight and lessons learned of the euphoria-filter, a robust H2/H∞ model-based FDI technique, with specific application to the Lisa-Pathfinder thruster failures.

A hybrid fault detection and isolation strategy for a team of cooperating unmanned vehicles

In this paper, a hybrid fault detection and isolation (FDI) methodology is developed for a team of cooperating unmanned vehicles. The proposed approach takes advantage of the cooperative nature of the team to detect and isolate relatively low-severity actuator faults that are otherwise not detectable and isolable by the vehicles themselves individually. The approach is hybrid and consists of both low-level (agent/team level) and high-level [discrete-event systems (DES) level] FDI modules. The high-level FDI module is formulated in the DES supervisory control framework, whereas the low-level FDI module invokes classical FDI techniques. By properly integrating the two FDI modules, a larger class of faults can be detected and isolated as compared to the existing techniques in the literature that rely on each level separately. Simulation results for a team of five unmanned aerial vehicles are also presented to demonstrate the effectiveness and capabilities of our proposed methodology.

Fault Detection and Isolation for Nonlinear System via ESO

In this paper, a fault detection and isolation system for nonlinear systems is presented. Fault detection and isolation is accomplished by using extended state observer (ESO) and fuzzy logic system. The major of observer-based fault detection methods rely on the accurate mathematical model of the system, but in the real world an accurate model of the system may not be available. The ESO is different from conventional observers, it does not require an accurate model of the system, it provides vital information for fault detection with only partial information of the plant. The ESO has ability to augment unknown dynamics combined with unknown external disturbance as extended state and estimate it in real time by using given input-output data. This paper presents a new sensor fault detection and isolation (FDI) via ESO and fuzzy logic system. A two-tank system is used as a case study. The simulation results confirm the simplicity and effectiveness of the proposed FDI technique.

Fault Detection and Isolation using an Adaptive Unscented Kalman Filter

Abstract An enhanced Fault Detection and Isolation (FDI) technique based on 't' statistical test is presented here employing a residual based R-Adaptive Unscented Kalman Filter (AUKF). Although the use of AUKF is common for estimation problem, employing AUKF for FDI duty is quite rare. Adaptive Unscented Kalman filtering framework is chosen here due to its derivative free calculations in the algorithm and reportedly better accuracy. Monitoring the 't' statistic of the innovation sequence, measurement faults are detected and corresponding alarm signals are generated here. The presented 't' statistic based FDI technique comprising of consistency checking and abruptness checking is straight forward, demands less computational burden and minimizes false alarms. The superiority of the proposed AUKF based FDI method compared to non adaptive, nonlinear filtering based FDI techniques is demonstrated here by an extensive simulation study executed on a nonlinear LEO satellite planar model. The use of AUKF has provided faster convergence speed and is also found to be advantageous for the situations where the measurement noise statistics are unknown.

Real Time Diagnosis in Wind Energy Conversion System

The current paper presents an FDI (Fault Detection and Isolation) technique applied to the Doubly Fed Induction Generator (DFIG) of a Wind Turbine (WT). The method is based on the use of a Generalized Observer Scheme (GOS) using a Kalman filter. The fault localization is performed by the Page-Hinkley's test. The whole procedure is validated using a numeric simulator, then a real benchmark emulating a wind turbine. Finally, real time tests are performed to allow real time validation.

Aircraft Power Generators: Hybrid Modeling and Simulation for Fault Detection

Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation (FDI) are necessary components for safe and reliable operation of the IDG and the aircraft. IDGs are complex systems, and a majority of the existing FDI techniques for the electrical subsystem (brushless generator) are based on signal analysis and heuristic methods derived from experience. Model-based fault diagnosis techniques are hypothesized to be more general and powerful in designing detection and isolation schemes. However, building sufficiently accurate models of brushless generators is a difficult task. dq models have been developed for single generators, but these models are not suitable to represent the complete brushless generator either in normal or fault situations, where the generator may become unbalanced. In this paper, we develop a novel hybrid dynamical model for the complete brushless ac generator. We exploit the hybrid modeling capability to accurately model different rectifier diode faults and rotor winding faults, reported as the most severe brushless generator faults. We simulate the hybrid model for nominal and different faulty conditions, and develop fault signatures for different machine faults.

Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing

There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.