Model-based Fault Detection Research Articles

Application of data-driven methods for energy system modelling demonstrated on an adaptive cooling supply system

The efficient and sustainable operation of building energy systems is playing an increasingly important role in most industrialized countries. At the same time, building energy systems are becoming increasingly complex; fault-free and optimal operation, under dynamic boundary conditions, is becoming more and more challenging. There are many approaches in research to address the optimal control problem of building energy systems, such as Rule-based Control, Model Predictive Control, or Adaptive Control. However, most methods rely on models of the system dynamics with high prediction accuracies. This is especially the case in Model Predictive Control, where the model is part of a continuously executed optimization problem; but models are also required when it comes to the optimal design of Rule-based Controllers, the safe pre-training of Adaptive Controllers, or model-based fault detection. A limiting factor for the manual development of physical models, for building energy systems, are the low monetary incentives for engineering services, due to the low energy prices in most countries. In addition, the creation of such models is time-consuming and error-prone, even for domain experts. Another weakness is that changes in the system dynamics are not automatically adapted within the models. These challenges are contrasted by an increasing availability of monitoring-data and computational power in recent years; with machine-learning algorithms, these resources are used in numerous application areas to achieve very promising results. Machine-learning methods can help to obtain data-driven, self-calibrating models, which can be learned from monitoring-data. In this paper, we apply methods for automated data-driven model generation. We demonstrate how machine-learning algorithms together with structured hyper-parameter tuning can be used to model individual subsystems as well as a complete energy supply system. To represent the dynamics of the supply system, it is first decomposed into simple functional relationships, which are aggregated into the overall system after training of the comparatively simple subsystem models. We evaluate the accuracy of the data-driven subsystem models using established metrics for the evaluation of regression models, namely the R2-score and the RMSE. The considered system is integrated into a district cooling network and consists of two compression chillers and an ice storage unit. Our investigations show that the dynamics of the subsystems can be learned with high accuracies, depending on the operation mode and the selected features. The prediction of the power demand of the compression chillers is learned with R2-scores between 0.94 and 0.99 and RMSE values between 2.02 kW and 3.51 kW. Also, the prediction of the percentage of ice formation within the ice storage is learned accurately with a R2-score of 1 and RMSE values between 0.08 % and 0.72 %. The dynamics of the aggregated system also show plausible behavior and can thus be used in future work. This work is part of an ongoing research project with the aim to optimize the operation of the entire campus cooling energy supply system. Our results show that, if detailed monitoring-data are available, data-driven modelling represents a viable alternative to the labor-intensive physical modelling approach. Furthermore, we emphasize the importance of structured hyper-parameter tuning, discuss the specifics of different machine-learning algorithms, and elaborate on possible future developments in this research area.

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

Actuator Fault Detection and Isolation for Helicopter Unmanned Arial Vehicle in the Present of Disturbance

Helicopter unmanned aerial vehicle (HUAV) are an ideal platform for academic researchs. Abilities of this vehicle to take off and landing vertically while performing hover flight and various flight maneuvers have made them proper vehicles for a wide range of applications. This paper suggests a model-based fault detection and isolation for HUAV in hover mode. Moreover in HUAV, roll, pitch and yaw actuator faults are coupled and affect each other, hence, we need a method that decouples them and also separates fault from disturbance. For this purpose, a robust unknown input observer (UIO) is designed to detect bias fault and also catastrophic fault such as stuck in actuators of HUAV. The robust UIO isolates roll and pitch actuator faults from yaw actuator fault. The novelty of this manuscript is the design of two UIO observers to detect and decouple the faults of helicopter actuators, one for lateral and longitudinal actuators and the other for pedal actuator. Also, the proposed method is compared with extended Kalman filter (EKF). Simulation results show effectiveness of the proposed method for detection and isolation of actuator faults with less number of observers and it is able to decouple fault and disturbance effects.

A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

Model-based fault detection filter for Markovian jump linear systems applied to a control moment gyroscope

We study a fault detection problem in the context of Markovian Jump Linear Systems (MJLS). The major contribution of this paper is the design of a mode-dependent mixed H∞/H− Fault Detection Filter (FDF) under the MJLS formulation, which is obtained by solving a Linear and Bilinear Matrices Inequalities (LMI / BMI) optimization problem. As a secondary contribution, we present a simulation of the theoretical results using a Control Moment Gyroscope unit, which is extensively used as an actuator for attitude control of spacecraft and satellites. The attitude control of satellites is a class of problem that is within the framework of MJLS, since it allows us to model the loss of communication between the various components of the network system. Therefore, designing Fault Detection Filters under this framework provides reliable solutions. From the simulations results, it is possible to observe that the proposed mixed H∞/H− fault detection solution outperformed the approach using only the H∞ norm, showing that it could be a valuable alternative for the fault detection problem.

Robust Three Stage Central Difference Kalman Filter for Helicopter Unmanned Aerial Vehicle Actuators Fault Estimation

This paper proposes state and fault estimations for uncertain time-varying nonlinear stochastic systems with unknown inputs. we suppose, the information about the fault and unknown inputs is not perfectly known. For this purpose, in this manuscript, we developed a robust three-stage central difference Kalman filter (RThSCDKF). We used RThSCDKF for model-based fault detection and identification (FDI) in nonlinear hover mode of helicopter unmanned aerial vehicle (HUAV) in the presence of external disturbance. In this system, actuator faults are affected by each other. The proposed method estimates and decouples actuator faults in the presence of external disturbances. This model can detect stuck and floating faults that are important to detect. At the end, this method is compared with the three-stage extended Kalman filter (ThSEKF). Simulation results show the effectiveness of the proposed robust method for detection and isolation of various actuator faults and also this shows more accuracy with respect to ThSEKF.

Modulation-Function-Based Finite-Horizon Sensor Fault Detection for Salient-Pole PMSM using Parity-Space Residuals

An online model-based fault detection and isolation method for salient-pole permanent magnet synchronous motors over a finite horizon is proposed. The proposed approach combines parity-space-based residual generation and modulation-function-based filtering. Given the polynomial model equations, the unknown variables (i.e. the states, unmeasured inputs) are eliminated resulting in analytic redundancy relations used for residual generation. Furthermore, in order to avoid needing the derivatives of measured signals required by such analytic redundancy relations, a modulation-function-based evaluation is proposed. This results in a finite-horizon filtered version of the original residual. The fault detection and isolation method is demonstrated using simulation of various fault scenarios for a speed controlled salient motor showing the effectiveness of the presented approach.

Online Model-Based Fault Detection of Synchronous Generators Using Residual Analysis

Online Model-Based Fault Detection of Synchronous Generators Using Residual Analysis

Model-Based Fault Detection and Isolation in DC Microgrids Using Optimal Observers

DC microgrids require advanced protection techniques for fault detection and isolation (FDI). In this work, an FDI method able to respond to different types of component faults is developed based on system modeling. First, the state-space representation of a multiterminal dc microgrid with component faults is derived. Then, an FDI function based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal {H}}_{-}/{\mathcal {H}}_{\infty }$ </tex-math></inline-formula> observers is designed. To achieve the desired selectivity in fault isolation, the linear matrix inequality (LMI) optimization approach is adopted in the observer design. The performance of the proposed FDI method is verified under the real-time (RT) simulation of a three-terminal low-voltage dc microgrid and with a small-scale laboratory dc grid. The proposed FDI method is proved to be effective to detect and isolate different faults in dc microgrids with a response time of 1 ms.

Stator-Winding Incipient Shorted-Turn Fault Detection for Motor System in Motorized Spindle Using Modified Interval Observers

As an important part of the power source, the motorized spindle is considered as the components in the high-end computer numerical control (CNC) machine tools, which is required as special attention to avoid expensive production shutdown due to the appearance of massive failures. Therefore, it is necessary to detect the incipient fault of the motorized spindle by establishing an appropriate model. This article presents a scheme for detecting the stator-winding incipient shorted-turn fault of the motor system in the motorized spindle. As the cornerstone of the model-based fault detection scheme, the electromagnetic coupling motor model in the motorized spindle with the stator-winding incipient shorted-turn fault under dq coordinate is given. In the presence of electromagnetic disturbance, two aspects can make the stator-winding shorted-turn fault of the motor in motorized spindle detected earlier. First, the modified interval observer based on the established mathematical model of the motor in the motorized spindle has more design freedom than the Luenberger interval observer, which can make the residual interval generated in the optimization process have better performance and a wider range of applications. Second, the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance is introduced in the process of residual interval generation, which can make it have better electromagnetic disturbance robustness and incipient fault sensitivity. The effectiveness of the proposed scheme is illustrated by numerical simulation and is further validated by lab experiments on a 9.8-kW motorized spindle test rig.

Theories for design and analysis of robust [formula omitted] fault detectors

This paper deals with model-based fault detection and isolation problems, for linear time invariant systems subject to a large class of uncertainties and disturbances. A new approach based on non-smooth optimization techniques, is proposed to synthesize a state–space realization of the fault detection filter, within the H∞/H− setting. A set of new criteria for robust fault detection performance analysis is too proposed, using the generalized structured singular value theory. The proposed theories are illustrated on a satellite example, under a large class of nonlinear dependent uncertainties. Through a deep analysis of the example, it is shown how the H∞/H− design – μg analysis tools can serve as a general theory, to solve fault detection problems.

Data-driven Residual Generation for Early Fault Detection with Limited Data

&#x0D; &#x0D; &#x0D; Traditionally, fault detection and isolation community have used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many complex systems it is not feasible to develop highly accurate models for the systems and to keep the models updated during the system lifetime. Recently, data- driven solutions have received an immense attention in the industrial applications for several practical reasons. First, these methods do not require the initial investment and expertise for developing accurate models. Moreover, it is possible to update and retrain the diagnosers as the system or the environment change over time automatically. Finally, unlike the model-based methods it is straightforward to combine time series measurements such as pressure and voltage with other sources of information such as system operating hours to achieve a higher accuracy. In this paper, we extend the traditional model- based fault detection and isolation concepts such as residuals, and detectable and isolable faults to the data-driven domain. We then propose an algorithm to automatically generate residuals from the normal operating data. We compare the performance of our proposed approach with traditional model-based methods through a case study.&#x0D; &#x0D; &#x0D;

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.

Dual-sliding mode approach for separated fault detection and tolerant control for functional safety of longitudinal autonomous driving

This paper describes model-based separated fault detection and fault tolerant control of longitudinal autonomous driving using dual-sliding mode observer for functional safety. Internal and environment sensors such as camera or radar are required to measure the acceleration information of the subject vehicle and the relative distance and velocity information between the preceding and subject vehicles in longitudinal autonomous driving. In order to detect the independent fault of each sensor, a dual-sliding mode observer (SMO) is used for fault reconstruction under the assumption that V2V (Vehicle to Vehicle) communication for vehicle driving state is available. The each SMO reconstructs the expected fault in sensor based on discontinuous injection term used for converging output error to zero. Based on the reconstructed fault by each SMO, faults are detected using threshold approach. When the fault is detected, the reconstructed fault is used for fault tolerant control by subtracting to faulty data. The proposed fault detection (FD) and fault tolerant control (FTC) algorithms were evaluated using actual driving data and a three-dimensional (3D) vehicle model with a linear quadratic regulator for following control. The evaluation results are presented and analyzed with regard to fault reconstruction, detection, and tolerant control in four cases wherein two types of faults were applied.

Plant Model-Based Fault Detection during Aircraft Takeoff Using Non-Deterministic Finite-State Automata

In this note, the application of a plant model-based fault detection method for nonlinear control systems on aircraft takeoff is introduced. This method utilizes non-deterministic finite-state automata, which approximate the fault-free dynamics of the plant. The aforementioned automaton is computed in a preliminary step while during evolution of the plant the automaton is continually evaluated to detect discrepancies between the actual and the nominal dynamics. In this way the fault detection module itself can be implemented on simpler hardware on board of the plant. Moreover, an implementation technique is presented that allows the use of the proposed fault detection method when the plant dynamics is given only by means of a graphical programming script. The great potential and practicality of the used method are demonstrated on a simulated takeoff manoeuvre of a battery-electrically driven aircraft.

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle.

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

Application of a Model-Based Fault Detection Approach on a Spacecraft

The model-based fault detection approach is one of the software-based supervision systems monitoring. This method has a marked effect to detect components fault without demanding extra sensors to measure or add redundancy. The extended multiple model’s adaptive estimation method is an online strategy to detect and isolation failure of components. Simple implementation, fast and accurate response, compatibility with nonlinear systems, and the ability to detect different types of faults are the most important features of this method. This method is applied to the faulty spacecraft in terms of actuators and its capability is evaluated. The most probable actuator fault implemented using MATLAB/SIMULINK software. The presented approach successfully detects faulty actuators.

Mitigating zero dynamic attack in communication link‐enabled droop‐controlled hybrid AC/DC microgrids

This article focuses on mitigation of zero dynamic attack in communication link-enabled droop-controlled hybrid AC/DC microgrids (MGs). To transmit setpoints for droop controllers and to also send measured AC and DC voltages and AC frequency in this sort of MGs, a communication link is required. Such links are exposed to cyber-attacks. First of all, this article tries to indicate that the system would be vulnerable to zero dynamic attack. In the second step, it is shown that zero dynamic attack can be mitigated by closing the secondary control loop. It is also shown that the attack can be distinguished from load/generation disturbances. In order to proceed with the challenge, the control signal in a closed loop system is used as the tricks of the trade. To achieve the goal, parity space as a kind of model-based fault detection approach is applied to a recently proposed dynamic model for droop-controlled hybrid AC/DC MGs. Evaluation of the detecting approach confirms that not only it can detect the attack effectively, but also it distinguishes from disturbance perfectly.

Hidden Markov Model Based Fault Detection for Networked Singularly Perturbed Systems

Based on a hidden Markov model (HMM), the issue of fault detection (FD) is investigated for singularly perturbed systems with their measurements transmitted over a bandwidth-limited communication network. A homogeneous Markov chain is adopted to model packet dropouts and time delays simultaneously, whose mode transition probabilities are assumed to be partially unknown. The discrepancies between the Markov modes and their observed ones have been noted, which are reflected by a hidden Markov process. An HMM-based FD filter (FDF) is aimed to be designed such that the stochastic stability and prescribed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance are ensured for the resulting filtering error dynamics of FD. A new Lyapunov-Krasovskii functional is constructed, which is with the Markov mode and the singular perturbation parameter (SPP). With the aid of up-to-date techniques in handing time delays, a sufficient condition based on linear matrix inequalities (LMIs) is derived which provides a design scheme of such an FDF. The FDF parameters are given and the SPP's admissible bounds are evaluated when the LMIs have feasible solutions. The performance of the designed FDF is demonstrated by two examples.

Detection of zone sensor and actuator faults through inverse greybox modelling

Zone-level sensor and actuator faults can substantially affect the energy and comfort performance of heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. As these faults leave their fingerprints on a building automation system (BAS), algorithms which use features generated from BAS data streams as symptoms to detect and isolate faults can be developed. This paper presents an inverse greybox model-based fault detection and diagnostics method for zone-level sensor and actuator faults. The method maps the BAS data to a simplified physical representation of the zone temperature and airflow response. If the parameter estimates of the greybox model are abnormal, we treated them as potential symptoms of zone-level sensor and actuator faults. The method was demonstrated on the BAS data from 35 rooms of an academic office building. Inverse greybox models with six sensor and three actuator regressors and nine parameters for each room were trained by using a genetic algorithm. Based on the findings of a point-by-point condition survey, it was identified that these rooms contained three perimeter heater, two variable air volume (VAV) unit damper, one VAV pressure sensor, and two VAV reheat coil faults. All except a reheat coil fault were correctly identified based on the interpretation of the physical significance of the greybox model parameters. The BAS data and verified fault states from the condition survey are made publicly available to support the further development and assessment of fault detection and diagnosis algorithms.