Model-based Fault Diagnosis Research Articles

Model-based fault diagnosis of Lithium-ion battery using strong tracking Extended Kalman Filter

The failure of battery could lead to serious inconvenience, performance deterioration, accelerated aging and costly maintenance. It is imperative to design a reliable and robust fault detection scheme to detect the underlying faults and improper operation. In this paper, a model based fault diagnosis method is proposed based on multiple equivalent circuit models (ECMs) and strong tracking extended Kalman filter (ST-EKF), where the multiple ECMs are designed to capture different failure modes. The ST-EKF is utilized to estimate the terminal voltages and generate residual signals online. Simulation experiments are used to validate the effectiveness of the proposed algorithm in detecting multiple battery fault scenarios.

A Novel Energy Efficiency Metric for Model-Based Fault Diagnosis of Telecommunication Central Offices

A novel energy metric is presented, to be adopted for monitoring and diagnosis of telecommunication (TLC) central offices (COFs). Such an activity is motivated by the TLC players need to substantially reduce their energy demand, both to increase their market competitiveness and meet the stringent green-house gas (GHG) emission regulations. The proposed metric, the utilization factor (UF), was thus defined according to the energy break-down of TLC-COFs. Then, suitable data-processing techniques were applied to develop a diagnosis-oriented UF model. Model accuracy, found to be always capable of guaranteeing UF estimation errors safely below 15 % for all non-faulty COFs, was proven adequate to perform model-based fault detection and isolation of relevant malfunctioning, such as abnormal data acquisition and non-optimal energy management.

Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries

The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may consequently introduce a mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct a model of Li-ion battery cells and to identify faulty operations in a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between the measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm is shown to be efficient in terms of the fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations.

Model-based fault diagnosis and prognosis of dynamic systems: a review

In maintenance of engineering systems, condition monitoring, fault diagnosis and fault prognosis constitute some of the principal tasks. With the increase of the number of machines within processing plants and their operational complexities, many engineers and researchers have started looking for automated solutions for these tasks. In most of the proposed solutions, these dynamic systems are modelled using tools like automata, Petri nets, bond graphs and Bayesian networks to diagnose and predict faults in those systems. This paper reviews these graphical model-based techniques related to fault diagnosis and prognosis and give suggestions for future research directions identifying research gaps in the field.

Model-Based Fault Diagnosis of an Anti-Lock Braking System via Structural Analysis

The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-determined (MSO) sets selection, and MSO-based residual design. In terms of the four faults in the ABS, they are evaluated to be detectable through performing a structural representation and making the Dulmage-Mendelsohn decomposition with respect to the fault modelling, and then they are proved to be isolable based on the fault isolability matrix via SA. After that, four corresponding residuals are generated directly by a series of suggested equation combinations resulting from four MSO sets. The results generated by numerical simulations show that the proposed FDI system can detect and isolate all the injected faults, which is consistent with the theoretical analysis by SA, and also eventually validated by experimental testing on the vehicle (EcoCAR2) ABS.

Reduced‐order dynamic model for droop‐controlled inverter/converter‐based low‐voltage hybrid AC/DC microgrids – Part 2: DC sub‐microgrid and power exchange

This study focuses on reduced-order dynamical modelling of droop controlled converter-based DC sub-microgrid (MG) in a hybrid AC/DC MG. In hybrid MGs, electrical power is exchanged between the AC and DC sub-MGs by a bidirectional AC/DC converter. The authors aim to develop a comprehensive reduced-order dynamical model for the DC side in this part, incorporating standard classes of electrical loads including constant current, constant power, and constant resistance loads. Furthermore, dynamical behaviour of the power exchange between the AC and DC sub-MGs is modelled, considering that the bidirectional power converter controller aims to equalise the load ratios of AC and DC sub-MGs in order to facilitate overall decentralised control over the hybrid MG. Analytical derivations of steady-state values of main variables are given and the overall dynamical and algebraic equations are determined. In order to validate the developed model, a hybrid MG is implemented in PSCAD. Then, the proposed model for the case study is implemented in Matlab/Simulink and the results are compared with the PSCAD outputs. The comparative results show the validity of the developed reduced-order comprehensive model. The reduced-order models are preferred in designing observers such as model-based fault detection and diagnosis observers.

An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica

Abstract The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers' poor judgment. This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts' involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components' control signal. The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems.

Framework for Unifying Model-based and Data-driven Fault Diagnosis

Model-based diagnosis methods rely on a model that defines nominal behavior of a dynamic system to detect abnormal behaviors and isolate faults. On the other hand, data-driven diagnosis algorithms detect and isolate system faults by operating exclusively on system measurements and using very little knowledge about the system. Recently, several researchers have combined model-based diagnosis techniques with datadriven approaches to propose hybrid1solutions for fault diagnosis. Many researchers have proposed methods to integrate specific approaches. In this paper, we demonstrate that data-driven and model-based diagnosis methods follow a similar procedure and can be represented by a general unifying framework. This unifying framework for fault detection and isolation can be used to integrate different methodologies developed by two communities. As a case study, we use the proposed framework to build a crossover solution for fault diagnosis in a wind turbine benchmark. In this case study, we show that it is possible to achieve a better diagnosis performance by using a hybrid method that follows the proposed framework.

Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles

The condition monitoring and fault diagnosis of the lithium-ion battery system are crucial issues for electric vehicles. The shocks, blows, twists, and vibrations during the electric vehicle driving process may cause the insulation fault. In order to ensure the safety of the drivers and passengers, a real-time monitor to detect the insulation state between the high voltage and ground is required. However, the conventional battery management system only provides very simple and coarse-grained measurements to detect the insulation resistance. In this work, a model-based insulation fault diagnosis method is proposed. Firstly, the equivalent circuit model for insulation fault diagnosis is established using a high-fidelity cell model. Then, the recursive least-squares method is employed to identify the model parameters. Considering the system nonlinear properties, measurement noise and unknown disturbance, the Kalman filter based state observer is designed for joint estimation of both the battery voltage and state-of-charge using the identified battery model. Finally, the positive and negative virtual insulation resistance are quantitatively assessed based on the prediction results of the state observer. Experiments under different loading profiles are performed to verify the proposed method.

Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors.

Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device.

Model-based analysis and fault diagnosis of a compound planetary gear set with damaged sun gear

The vibration properties of compound planetary gears are more complicated than that of simple ones. This paper aims to investigate the fault properties of a compound planetary gear set in chipped sun gear conditions using model-based method. A three-dimensional lumped-parameter nonlinear dynamic model for the compound planetary gear set is established. This model considers the time-varying mesh stiffness (TVMS), the mesh phase relations, and gear chipping defects. The analytical equations are derived to quantify the TVMS reduction induced by the chipped gear based on the improved potential energy method. Further, the simulations are performed to demonstrate the fault features of sun gears with single or multiple chipped teeth in different gear stages. Moreover, the theoretical derivations are validated through the experimental signals analysis.

A simulation model based fault diagnosis method for bearings

A simulation model based fault diagnosis method for bearings

Fault Detection and Classification for Nonlinear Chemical Processes using Lasso and Gaussian Process

This paper presents a statistical monitoring methodology to identify and diagnose intermittent stochastic faults occurring in a nonlinear dynamic chemical process. This methodology addresses three important aspects in model-based fault detection and diagnosis (FDD): model simplicity, interpretability, and calibration. The goal is to generate a surrogate model that can be easily interpreted while maintaining model flexibility and efficiency. The key feature is the use of an active set optimization in combination with a Gaussian process (GP) model for fault detection and classification. To optimally select measured variables for inferring faults, an active set optimization with l1-norm regularization is combined with statistical analysis. This can provide a trade-off between model dimensionality and model prediction error. To ensure sufficient data for the calibration of GP models, an improvement in a probability-based model adjustment algorithm is developed. The performance of the developed FDD scheme is i...

A survey on model-based fault diagnosis for linear discrete time-varying systems

Abstract To meet the rising demands for safety and reliability of modern industrial control systems, the model-based fault diagnosis problem has attracted much attention in the past few decades both from the academic communities and in practical applications. Among the rich body of literature, the majority of the results are proposed for linear time invariant systems despite the fact that most practical processes are inherently time-varying by nature and discretized for online implementations. Recently, with the ever-increasing system complexity and scale, the model-based fault diagnosis issue for linear discrete time-varying (LDTV) systems has become a hot research topic of both theoretical importance and practical significance, and a great number of results have been reported with many open problems deserving further investigation. To reflect the latest progress in this emerging research branch, this survey aims to provide a timely reference to readers interested in this topic. More specifically, in this survey, the techniques for model-based fault diagnosis for LDTV systems are classified into observer-based methods, parity space-based approaches and parameter estimation schemes, which constitute the three main parts of this survey. The background and the latest progress for each class of these techniques are comparatively discussed with particular focuses on the fault detection and the fault estimation issues. Additionally, an overview on their practical applications to specific industrial plants is provided. Furthermore, the possible future research directions concerning the model-based fault diagnosis for LDTV processes are pointed out, followed by comprehensive concluding remarks.

Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm

Abstract The spontaneous internal short circuit that sporadically occurs during operation is an unsolved safety problem that hinders the widespread application of lithium ion batteries. An online fault-diagnosis algorithm is an urgent requirement for early detection of the spontaneous internal short circuit of lithium-ion batteries to guarantee safe operation. This paper presents a model-based fault-diagnosis algorithm for online internal-short-circuit detection. Relying on the theory of model-based control, the algorithm transforms the measured voltage and temperature to the intrinsic electrochemical status that can reflect typical internal-short-circuit features, i.e. the excessive depletion of capacity and abnormal heat generation. The estimated status of the suspicious cell deviates from the average value of the battery pack, therefore the algorithm can capture the internal-short-circuit fault by evaluating the levels of deviation. Simultaneously considering the diagnosis result calculated from both the voltage and temperature signal helps enhance the robustness of the algorithm with few false alarms. Substitute internal-short-circuit tests confirm that the algorithm is capable of identifying the internal-short-circuit fault before it develops into a severe hazard, e.g., thermal runaway. The equivalent short resistance, which can reflect the level of the internal short circuit, can be estimated with small error by the online fault-diagnosis algorithm.

A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application

Abstract: The impact of SOC and temperature on external short circuit (ESC) faults characteristics of lithium-ion batteries, including the current and voltage variation and temperature increase, are analyzed. A fractional-order model (FOM) and a first-order RC model are both employed to describe the electrical behavior of the battery cells with the ESC fault. While the model parameters are identified by the genetic algorithm (GA). A comparison study is made on the prediction accuracy for the two models. An effective classification method based on a random forests (RF) model is proposed to recognize the electrolyte leakage behavior that occurs during the ESC fault experiments. Based on the above efforts, the three steps model-based diagnosis algorithm for identifying the ESC fault and even electrolyte leakage of the battery in real-time is proposed. Two indicators of the root mean square error (RMSE) of battery predicting voltage are applied to diagnose for the ESC fault only and ESC-leakage merged fault. The result of the leakage condition is obtained by a pre-trained RF classifier to confirm the leakage detection result based on the RMSE indicator. Several cases are verified that all the ESC cells can be diagnosed efficiently.

Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

Fault detection and diagnosis for industrial systems has been an important field of research during the past years. Among these systems, the Tennessee Eastman process is extensively used as a realistic benchmark to test and compare different fault detection and diagnosis strategies. In this context, data-driven approach has been widely applied for fault detection and diagnosis of the Tennessee Eastman process, by exploiting the massive amount of available measurement data. However, only few published works had attempted to deal with the dynamic behavior of the whole system including the mixing zone, circulating pumps, the reactor, the separator, the stripper, and so on, because of the difficulty of modeling physical phenomena that may occur in such complex system. In this article, an accurate model of the Tennessee Eastman process, properly tailored for fault detection and diagnosis purposes, is provided. This model shows better fault detection and diagnosis performances than all the others proposed in the literature and gives better or comparable results with the data-driven approaches. This work uses the bond graph methodology to systematically develop computational and graphical model. This methodology provides a physical understanding of the system and a description of its dynamic behavior. The bond graph model is then used for monitoring purposes by generating formal fault indicators, called residuals, and algorithms for fault detection and diagnosis. Hence, abnormal situations are detected by supervising the residuals’ evolution and faults are isolated using the nature of the violated residuals. Therefore, the dynamic model of the Tennessee Eastman process can now be used as a basis to achieve accurately different analysis through the causal and structural features of the bond graph tool.

Formulation of five degrees of freedom ball bearing model accounting for the nonlinear stiffness and damping of elastohydrodynamic point contacts

The knowledge of the dynamic behavior of rolling elements bearings and the contact properties have shown to be essential for model based fault diagnosis, lifetime estimation and reduction of noise in several industrial equipment application. This paper formulates the force and moments equilibrium of an angular contact ball bearing with five degrees of freedom accounting for the effects of the elastohydrodynamic (EHD) lubrication of its elements. A complete nonlinear model is derived and equivalent parameters for stiffness and damping of each contact were evaluated for different loading conditions. An iterative solution process was proposed to couple the bearing equilibrium and the EHD contact calculation, so as to generate the most suitable representation of the lubrication condition at the equilibrium point. The resulting reduced order model is promising to save computational costs and to make feasible the analysis of complex rotating systems supported by oil lubricated angular contact ball bearings.

Optimal Fault Detection and Diagnosis in Transcriptional Circuits Using Next-Generation Sequencing.

We propose a methodology for model-based fault detection and diagnosis for stochastic Boolean dynamical systems indirectly observed through a single time series of transcriptomic measurements using Next Generation Sequencing (NGS) data. The fault detection consists of an innovations filter followed by a fault certification step, and requires no knowledge about the possible system faults. The innovations filter uses the optimal Boolean state estimator, called the Boolean Kalman Filter (BKF). In the presence of knowledge about the possible system faults, we propose an additional step of fault diagnosis based on a multiple model adaptive estimation (MMAE) method consisting of a bank of BKFs running in parallel. Performance is assessed by means of false detection and misdiagnosis rates, as well as average times until correct detection and diagnosis. The efficacy of the proposed methodology is demonstrated via numerical experiments using a p53-MDM2 negative feedback loop Boolean network with stuck-at faults that model molecular events commonly found in cancer.