Model-based Fault Detection Technique Research Articles

Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.

Model-based fault detection in photovoltaic systems: A comprehensive review and avenues for enhancement

Solar photovoltaic (PV) systems have become a vital renewable energy source, witnessing rapid global demand. Nevertheless, these systems are susceptible to faults and anomalies that can deteriorate performance and yield significant consequences. Hence, this paper is dedicated to reviewing recent advancements in monitoring, modeling, and fault detection methods for PV systems. It encompasses diverse PV system types, including grid-connected, stand-alone, and hybrid configurations, and delves into the latest data acquisition and monitoring techniques. The review also discusses various performance modeling approaches, including empirical, analytical, and numerical models, highlighting the significance of model validation and calibration. Furthermore, it provides a comprehensive analysis of model-based fault detection techniques. Overall, this paper underscores the pivotal role of fault detection in PV systems and offers a thorough comprehension of available techniques vital for enhancing system management and maintenance.

A Survey on Model-Based Fault Detection Techniques for Linear Time-Invariant Systems with Numerical Analysis

With the ongoing increase in complexity, less tolerance to performance degradation and safety requirements of practical systems has increased the necessity of fault detection (FD) as early as possible. During the last few decades, many research findings have been developed in fault diagnosis that addresses the issue of fault detection and isolation in linear and nonlinear systems. The paper’s objective is to present a survey on various state-of-art model-based FD techniques developed for linear time-invariant (LTI) systems for the interested readers to learn about recent development in this field. Model-based FD techniques for LTI systems are classified as parameter-estimation methods, parity-space-based methods, and observer-based methods. The background and recent progress, in context to fault detection, of each of these methods and their practical applications are discussed in this paper. Furthermore, two different FD techniques are compared via analytical equations and simulation results obtained from the DC motor model. In the end, possible future research directions in model-based FD, particularly for the LTI system, are highlighted for prosperous researchers. A comparison and emerging research topic make this contribution different from the existing survey papers on FD.

Evaluation of the Correlation Coefficient as a Prognostic Indicator for Electromechanical Servomechanism Failures

In order to identify incipient failures due to a progressive wear of a primary flight command electromechanical actuator, several approaches could be employed; the choice of the best ones is driven by the efficacy shown in fault detection/identification, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able to identify the precursors of the abovementioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming malfunction and alerting the maintenance crew such to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a fault detection/identification technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior, and to evaluate its potential use as prognostic indicator for the considered progressive faults (i.e. frictions and mechanical backlash acting on transmission, stator coil short circuit, rotor static eccentricity). To this purpose, an innovative model based fault detection technique has been developed merging several information achieved by means of Fast Fourier Transform (FFT) analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the performance of the proposed technique, an appropriate simulation test environment was developed: the results showed an adequaterobustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures.

Improved model based fault detection technique and application to humanoid robots

One of the issue of significant interest for robotics is the fault detection, specifically when we have application in risky circumstances. Robotic systems required a capacity to efficiently identify and endure some defects so that they can keep achieving the required tasks while avoiding instantaneous repairing process. Consequently, we aim in this work to propose a systematic approach for state estimation and fault detection technique to enhance the operation of humanoid robots (HR) systems using an extended Kalman filter (EKF)-based multiscale optimized exponentially weighted moving average chart (MS-OEWMA). The objectives of this work are sixfold: (1) apply EKF technique to estimate the state variables in HR systems. The EKF is among the most popular nonlinear state estimation methods; (2) use dynamical multiscale representation for obtaining accurate settled characteristics; (3) propose a new optimized EWMA (OEWMA) based on the best selection of both smoothing parameter (λ) and control width L; (4) combine the advantages of state estimation technique with MS-OEWMA chart to improve the monitoring of HR systems; (5) investigate the effect of fault types (change in variance and mean in shift) and fault sizes on the monitoring performances; (6) validate the developed technique using two robot models: inverted pendulum and five-bar linkage. The detection results are evaluated using three fault detection metrics: missed detection rate (MDR), false alarm rate (FAR) and out-of-control average run length (ARL1).

Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis

The model-based fault detection technique, which needs to identify the system models, has been well established. The objective of this paper is to develop an alternative procedure instead of identifying the system models. In this paper, subspace method aided data-driven fault detection based on principal component analysis (PCA) is proposed. The basic idea is to use PCA to identify the system observability matrices from input and output data and construct residual generators. The advantage of the proposed method is that we just need to identify the parameterized matrices related to residuals rather than the system models, which reduces the computational steps of the system. The proposed approach is illustrated by a simulation study on the Tennessee Eastman process.

Practical design considerations for successful industrial application of model-based fault detection techniques to aircraft systems

This paper discusses some key factors which may arise for successful application of model-based Fault Detection (FD) techniques to aircraft systems. The paper reports on the results and the lessons learned during flight V&V (Validation & Verification) activities, implementation in the A380 Flight Control Computer (FCC) and A380 flight tests at Airbus (Toulouse, France). The paper does not focus on new theoretical materials, but rather on a number of practical design considerations to provide viable technological solutions and mechanization schemes. The selected case studies are taken from past and on-going research actions between Airbus and the University of Bordeaux (France). One of the presented solutions has received final certification on new generation Airbus A350 aircraft and is flying (first commercial flight: January 15, 2015).

Signal and model-based fault detection for aircraft systems

The paper presents several examples on model and signal based fault detection in aircraft Electrical Flight Control System (EFCS). The examples are taken from past and on-going research projects and actions between Airbus Group and the University of Bordeaux (France). The techniques presented have been tested on highly representative aircraft benchmarks, real flight data, or through realtime implementation on Airbus test facilities and real A380 flight tests. The focus is on specific problems associated with each design when it comes to providing viable technological solutions and mechanization schemes. One of the proposed model-based solutions has received certification on new generation Airbus A350 aircraft. It is a great achievement for SAFEPROCESS academic community as it demonstrates that a model-based fault detection technique is now flying (first commercial flight: January 15, 2015).

Experimental application of nonlinear minimum variance estimation for fault detection systems

The purpose of this paper is to present an experimental design and application of a novel model-based fault detection technique by using a nonlinear minimum variance (NMV) estimator. The NMV estimation technique is used to generate a residual signal which is then used to detect faults in the system. The main advantage of the approach is the simplicity of the nonlinear estimator theory and the straightforward structure of the resulting solution. The proposed method is implemented and validated experimentally on DC servo system. Experimental results demonstrate that the technique can produce acceptable performance in terms of fault detection and false alarm.

Definition of parametric methods for fault analysis applied to an electromechanical servomechanism affected by multiple failures

In order to detect incipient failures due to a progressive wear of a primary flight command electromechanical actuator, prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able to identify the precursors of the above mentioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew such to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a prognostic technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior; in particular four kinds of failure are considered: friction, backlash, coil short circuit, rotor static eccentricity. To this purpose, an innovative model based fault detection technique has been developedmerging together several information achieved by means of FFT analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the robustness of the proposed technique, an appropriate simulation test environment was developed. The results showed an adequate robustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures.

A hybrid model-based fault detection strategy for air handling unit sensors

For the air handling unit (AHU) sensor fault detection, the classical fault detection methods based on statistical residual evaluation are difficult to detect small bias fault especially under noisy conditions. On the other hand, a novel technique using fractal correlation dimension (FCD) algorithm can identify a tiny variation of curve fractal characteristic but needs a period of time. To balance their strengths as well as weaknesses, a hybrid model-based fault detection technique is developed by combining these two methods. The simulated data obtained from the TRNSYS simulation platform are used to validate the hybrid fault detection strategy. And a prediction model using support vector regression (SVR) is developed to obtain the fault-free references. Under a noise ranging from −0.3°C to +0.3°C, the technique is validated to detect six fixed biases of the supply air temperature sensor under three different load conditions. Given a specified threshold, the hybrid technique can identify the large bias faults such as ±0.5°C by statistical residuals and can detect small faults of ±0.2°C by FCD deviations. For the dynamic and nonlinear systems, FCD-based approach is more suitable for fault detection than for fault diagnosis.

Robust Fault Detection System for Insulin Pump Therapy Using Continuous Glucose Monitoring

The popularity of continuous subcutaneous insulin infusion (CSII), or insulin pump therapy, as a way to deliver insulin more physiologically and achieve better glycemic control in diabetes patients has increased. Despite the substantiated therapeutic advantages of using CSII, its use has also been associated with an increased risk of technical malfunctioning of the device, which leads to an increased risk of acute metabolic complications, such as diabetic ketoacidosis. Current insulin pumps already incorporate systems to detect some types of faults, such as obstructions in the infusion set, but are not able to detect other types of fault such as the disconnection or leakage of the infusion set. In this article, we propose utilizing a validated robust model-based fault detection technique, based on interval analysis, for detecting disconnections of the insulin infusion set. For this purpose, a previously validated metabolic model of glucose regulation in type 1 diabetes mellitus (T1DM) and a continuous glucose monitoring device were used. As a first step to assess the performance of the presented fault detection system, a Food and Drug Administration-accepted T1DM simulator was employed. Of the 100 in silico tests (10 scenarios on 10 subjects), only two false negatives and one false positive occurred. All faults were detected before plasma glucose concentration reached 300 mg/dl, with a mean plasma glucose detection value of 163 mg/dl and a mean detection time of 200 min. Interval model-based fault detection has been proven (in silico) to be an effective tool for detecting disconnection faults in sensor-augmented CSII systems. Proper quantification of the uncertainty associated with the employed model has been observed to be crucial for the good performance of the proposed approach.

Fault diagnostic systems for agricultural machinery

Fault detection and diagnosis in process industry have attracted a lot of attention recently. There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial and statistical methods. From a modelling perspective, the methods can rely on quantitative, semi-quantitative and qualitative models. At the other end of the spectrum, there are historical data-based methods that do not make use of any form of model information but rely only on historical process data. The basic aim of this study is to emphasize the importance of introducing more advanced multivariate fault diagnostic systems on agricultural machinery. Up till now, farmers and contractors still observe the process in order to detect process and sensor failures which can disturb the actions of the controllers and cause severe damage to the machine. In the future, the complete reliance on human operators for the correct functioning of these systems will become too risky, due to the increasing complexity of this type of machinery. A systematic and comparative study of various fault diagnostic methods, from an agricultural machinery perspective, is provided in this study. The different fault diagnostic techniques, investigated in scientific literature, are compared and evaluated on a common set of criteria. Typical requirements of a fault diagnostic system for agricultural machinery are adaptability to process changes, user-friendliness, quick detection and robustness. Based on these findings, a hybrid framework of qualitative model-based fault detection techniques and pattern recognition-based methods, which rely on historical process data, is proposed as the most suitable fault diagnostic technique. As a first step towards more advanced fault detection and isolation systems, the general applicability of intelligent neural network techniques like supervised self-organizing maps (SOMs) and back-propagation neural networks is illustrated for the detection and isolation of sensor failures on a New Holland CX combine harvester. Pattern recognition techniques, such as neural networks, were found to be very suitable for this kind of application because a lot of historical process data is available since the recent generation of combine harvesters is equipped with a wide range of sensors and actuators, which are continuously monitored. Moreover, these pattern recognition techniques allow quick detection, are easy to use and are able to adapt their structure and/or model parameters based on new measurement data. Since there is room for improvement of these standard techniques, suggestions for future research concerning fault diagnosis on agricultural machinery are given as well.

Condition monitoring of rail vehicle suspensions based on changes in system dynamic interactions

A novel scheme for the fault detection and condition monitoring of vehicle suspensions is presented in this study. The new technique exploits the dynamic interactions between different vehicle modes caused by component failures in the system, leading to a simple but effective solution. Compared with many model-based fault detection techniques, the proposed technique does not require complex mathematical models of the system and it overcomes potential difficulties associated with nonlinearities and parameter variations in the system. The use of inexpensive inertial sensors and ease of tuning make the practical implementation of the proposed scheme straightforward. A conventional railway vehicle is used in the study to illustrate the basic ideas as well as the effectiveness of the novel fault detection method, although the general principle is applicable to other systems.

New Horizons on Predictive Maintenance

This paper deals with predictive maintenance and its importance on industrial applications. After introduction, brief information is given about causes of electrical motor failures and then, predictive maintenance concept is introduced. Next, most popular techniques are told briefly. Model based fault detection technique which is utilized by Artesis MCM (Motor Condition Monitor) unit is mentioned. A case study is given at the end of the paper.

Generating Diagnostic Residuals for Steer-by-Wire Vehicles

Steer-by-wire remains unavailable on production vehicles today, despite the numerous benefits it will provide, due to the expense of high-reliability designs. Model-based fault detection techniques can eliminate the need for redundant sensors in steer-by-wire vehicles, lowering costs without compromising reliability. This paper presents the modeling and fault detection filter design techniques used to construct a set of diagnostic residuals suitable for detecting and isolating a wide variety of steering system faults, based only on measurements from sensors already present in a steer-by-wire system. These diagnostic residuals are sensitive enough to detect faults at a magnitude well below where they would impact the driver's ability to control the vehicle. Experimental results from a full-scale research steer-by-wire vehicle demonstrate this sensitivity

A vapour compression chiller fault detection technique based on adaptative algorithms. Application to on-line refrigerant leakage detection

The existence of faults in vapour compression chillers plays a significant role in terms of energy efficiency loss, performance degradations, and even environmental implications. In this paper, a dynamic model-based fault detection technique suitable for real-time implementation is proposed. The main objective is to obtain a reliable and automated tool for fault detection in vapour compression chillers, which can be applied in steady-state or transient operation. The fault detection methodology is based on comparing actual and expected performance using an adaptative model and operating variables dynamic thresholds. The technique has been successful applied for on-line refrigerant leakage detection with experimental tests involving the artificial introduction of the fault in a laboratory vapour compression plant, showing the results its capability of detecting incipient leakage failure conditions avoiding false alarms.

Multi-linear model-based fault detection during process transitions

Process transitions due to startup, shutdown, product slate changes, and feedstock changes are frequent in the process industry. Experienced operators usually execute transitions in the manual mode as transitions may involve unusual conditions and nonlinear process behavior. Processes are therefore more prone to faults as well as inadvertent operator errors during transitions. Fault detection during transition is critical as faults can lead to abnormal situations and even cause accidents. This paper proposes a model-based fault detection scheme that involves decomposition of nonlinear transient systems into multiple linear modeling regimes. Kalman filters and open-loop observers are used for state estimation and residual generation based on the resulting linear models. Analysis of residuals using thresholds, faults tags, and logic charts enables on-line detection and isolation of faults. The multi-linear model-based fault detection technique has been implemented using Matlab and successfully tested to detect process faults and operator errors during the startup transition of highly nonlinear pH neutralization reactor in the laboratory.

Particle filtering-based fault detection in non-linear stochastic systems

Much of the development in model-based fault detection techniques for dynamic stochastic systems has relied on the system model being linear and the noise and disturbances being Gaussian. Linearized approximations have been used in the non-linear systems case. However, linearization techniques, being approximate, tend to suffer from poor detection or high false alarm rates. A novel particle filtering based approach to fault detection in non-linear stochastic systems is developed here. One of the appealing advantages of the new approach is that the complete probability distribution information of the state estimates from particle filter is utilized for fault detection, whereas, only the mean and covariance of an approximate Gaussian distribution are used in a coventional extended Kalman filter-based approach. Another advantage of the new approach is its applicability to general non-linear system with non-Gaussian noise and disturbances. The effectiveness of this new method is demonstrated through Monte Carlo simulations and the detection performance is compared with that using the extended Kalman filter on a non-linear system.

Development of ARX model based off-line FDD technique for energy efficient buildings

Tools available for energy conservation in buildings still lack practical and cost-effective fault detection and diagnosis technique. This paper puts forth model based fault detection technique for off-line analysis based upon Autoregressive Exogenous method. Real time operation data of a Variable Air Volume Air Handling Unit in normal operating condition with artificially induced faults and without faults are obtained to validate the model. It is concluded that off-line analysis of data by this model is likely to detect most of the faults such as problems in damper control. The method seems to be robust until on-line fault detection and diagnosis techniques are developed.