Model-based Diagnosis Approach Research Articles

Joint feedback control and fault diagnosis disambiguation

This paper proposes a model-based diagnosis approach to detect and isolate intermittent faults in complex systems that operate under feedback control. The feedback control attempts to compensate for model uncertainties and deviations from nominal behavior, but these uncertainties are crucial for accurate fault diagnosis. We focus on faults that are observable only in a particular region of the state space, which is rarely reached in nominal behavior. To address this, we present an approach that considers both control requirements and diagnosis uncertainty in an optimization problem similar to model-predictive control. We compute perturbations on control signals that forces the system to reach states where faults are detectable. We apply our approach to a quadrotor system under motion feedback control, demonstrating the effectiveness of our method. Our approach has the potential to improve the resilience of complex systems by quickly detecting and recovering from disruptive events.

Implementation of a Cascade Fault Tolerant Control and Fault Diagnosis Design for a Modular Power Supply

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method.

DiagDO: an efficient model based diagnosis approach with multiple observations

DiagDO: an efficient model based diagnosis approach with multiple observations

Permanent Magnet Synchronous Motor Driving Mechanical Transmission Fault Detection and Identification: A Model-Based Diagnosis Approach

This paper presents a model-based scheme for permanent magnet synchronous motor (PMSM) driving transmission fault detection and identification (FDI) in a steady-state condition. The proposed framework utilizes a PMSM state-space model and an approximated transmission model to construct the regression models for parameter estimation using the Recursive Least-Square (RLS) algorithm. The FDI are accomplished by the residual current spectrum thresholding method to assess the fault characteristic frequency magnitude and also by parameter clustering. Two types of mechanical transmission with three different fault conditions are tested in the experiments. As a preliminary effort in the condition monitoring of PMSM driving transmission, the study results demonstrate a promising approach by considering both residual current spectrum and parameter cluster, which achieved a satisfactory decision making in detecting and identifying the faulty condition.

A Hybrid Model-Based Diagnosis Approach for Open-Switch Faults in PMSM Drives

In this letter, a hybrid model-based diagnosis method is proposed for open-switch faults in permanent magnet synchronous motor drives by combining the advantages of both model predictive control (MPC) and mixed logical dynamic (MLD) models. This idea is based on the receding optimization of the MPC and the estimating capability of the MLD. The predictive current model and the MLD model are established to predict and estimate phase currents, respectively. Besides, the logical vectors are theoretically investigated under various faulty conditions. Derived from the cost function, a diagnosis function is designed to evaluate the consistency between the actual and estimated currents. Consequently, the logical vector minimizing the diagnosis function is selected to locate faulty switches. Finally, experiments are conducted to verify the effectiveness of the proposed method.

Fault Diagnosis and Prognosis of a Brushless DC motor using a Model-based Approach

This paper proposes a model-based fault diagnosis and prognosis approach applied to brushless DC motors (BLDC). The objective is an early detection of mechanical and electrical faults in BLDC motors operating under a variety of operating conditions. The proposed model-based method is based on the evaluation of a set of residuals that are computed taking into account analytical redundancy relations. Fault diagnosis consist of two steps: First, checking if at least one of the residuals is inconsistent with the normal operation of the system. And, second, evaluating the set of the residuals that are inconsistent to determine which fault is present in the system. Fault prognosis consists of the same two steps but instead of considering current inconsistencies evaluates drift deviations from nominal operation to predict futures residual inconsistencies and therefore predict future fault detections and diagnosis. A description of various kinds of mechanical and electrical faults that can occur in a BLDC motor is presented. The performance of the proposed method is illustrated through simulation experiments.

Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory

Currently, detecting and isolating faults in hybrid systems is often done manually with the help of human operators. In this paper we present a novel model-based diagnosis approach for automatically diagnosing hybrid systems. The approach has two parts: First, modelling dynamic system behaviour is done through well-known state space models using differential equations. Second, from the state space models we calculate Boolean residuals through an observer-pattern. The novelty lies in implementing the observer pattern through the use of a symbolic system description specified in satisfiability theory modulo linear arithmetic. With this, we create a static situation for the diagnosis algorithm and decouple modelling and diagnosis. Evaluating the system description generates one Boolean residual for each component. These residuals constitute the fault symptoms. To find the minimum cardinality diagnosis from these symptoms we employ Reiter’s diagnosis lattice.For the experimental evaluation we use a simulation of the Tennessee Eastman process and a simulation of a four-tank model. We show that the presented approach is able to identify all injected faults.

Bank of Extended Kalman Filters for Faults Diagnosis in Wind Turbine Doubly Fed Induction Generator

In order to increase the efficiency, to ensure availability and to prevent unexpected failures of the doubly fed induction generator (DFIG), widely used in speed variable wind turbine (SVWT), a model based approach is proposed for diagnosing stator and rotor winding and current sensors faults in the generator. In this study, the Extended Kalman Filter (EKF) is used as state and parameter estimation method for this model based diagnosis approach. The generator windings faults and current instruments defects are modelled, detected and isolated with the use of the faults indicators called residuals, which are obtained based on the EKF observer. The mathematical model of DFIG for both healthy and faulty operating conditions is implemented in Matlab/Simulink software. The obtained simulation results demonstrate the effectiveness of the proposed technique for diagnosis and quantification of the faults under study.

Distributed Diagnosis of Electric Aircraft Powertrain

Electrically powered autonomous aircraft are being increasingly considered for intra-city short-haul air-taxi services to fly human passengers between different locations. As a result, it is critical to incorporate safety under autonomous operations into system operations by enabling such autonomous aircraft to make accurate estimates of its current health state and take the right decisions to complete its mission successfully. The first step to assess health state of the entire aircraft is for the aircraft to be able to assess the health of its individual critical systems, (e.g electrical powertrain) for it to be able to fly and reach its destination in a safe manner. The fundamental components of a powertrain in an electrically powered aircraft include key electrical components such as batteries, motors, and power electronics (e.g., electronic speed controllers). A model-based diagnosis approach of complex critical systems enables their safe and efficient operation. Typically, such model-based schemes are centralized approaches that suffer from inherent disadvantages such as computational complexity, single point of failure, and scalability issues. Distributing the diagnosis task addresses these issues. To this end, this paper presents the results of implementing a distributed diagnostics approach to a representative electric aircraft powertrain. In particular, we focus on the implementation of a distributed diagnosis framework to a quadrotor vehicle. The simulation experiments demonstrate how the distributed diagnosis algorithm correctly and efficiently diagnose faults in the rotorcraft’s electric powertrain.

A methodology for monitoring smart buildings with incomplete models

This paper combines a residual-based diagnosis approach and an unsupervised anomaly detection method for monitoring and fault diagnosis in smart buildings. Typically buildings are very complex, and it is computationally intractable to built accurate diagnosis models for large buildings. However, complete and fairly accurate models can be constructed for components like supply and exhaust fans in buildings. Our proposed method combines a model-based diagnosis approach that uses available models and a data driven approach that uses machine learning techniques along with the additional sensor data available from buildings to update a diagnosis reference model and build more complete diagnosis systems for buildings. To estimate the likelihood of each potential fault in the complex systems, the dependencies between components and, therefore, the sensor measurements need to be considered for accurate diagnosis. In this work, we employ the tree augmented naive Bayesian learning algorithm (TAN) to develop classifiers for fault detection and isolation. TAN structures can accommodate some dependencies between the measurements. We demonstrate and validate the proposed approach using a data-set from an outdoor air unit (OAU) system in the Lentz public health center in Nashville.

A novel approach for improving quality of health state with difference degree in circuit diagnosis

Model-based diagnosis (MBD) has been widely acknowledged to be an effective fault diagnosis paradigm for combinational circuits. Most diagnosis algorithms (DAs) return a single diagnosis, a list of diagnoses or a score for every returned diagnosis, which estimates the likelihood of each diagnosis to be correct. Roni Stern et al. recently proposed the heath state as an output of DA to provide a manageable view of which components are likely to be faulty for human operators in the circuit diagnosis area. The health state can be used widely in areas such as troubleshooting problems, among others. This paper proposes a novel approach that improves the quality of health state with difference degree (IHSD) utilizing the MBD approach with multiple observations in the circuit diagnosis area, which returns a list of possible diagnoses that explain these observations simultaneously. In addition, we also propose a multi-observation “scoring” method based on the difference degree for every returned diagnosis, where the difference degree denotes the “distance” from the multi-observations diagnosis to the relevant diagnosis that satisfies only one observation. We present evidence that shows that, compared with the state-of-the-art algorithm on health state (Stern et al., Artif Intell 248:26–45 2018), our approach improves the quality of health state.

Efficient zonal diagnosis with maximum satisfiability

Model-based diagnosis (MBD) has been widely acknowledged as an effective diagnosis paradigm. However, for large scale circuits, it is difficult to find all cardinality-minimal diagnoses within a reasonable time. This paper proposes a novel method that takes a significant step in this direction. The idea is to divide a circuit into zones and compute the cardinality-minimal diagnoses by finding subset-minimal diagnoses with cardinality-minimal via a maximum satisfiability (MaxSAT) solver on an abstracted circuit that is composed of these zones instead of all components. We also propose a new propagate-extend method for extending the seed-TLDs to obtain all cardinality-minimal diagnoses efficiently. We implement our method with a state-of-the-art core-guided MaxSAT solver, and present evidence that it significantly improves the diagnosis efficiency on ISCAS-85 circuits. Our method outperforms SATbD, which was recently shown to outperform most complete MBD approaches using satisfiability (SAT).

Model-Based Diagnosis of Multi-Track Level Crossing Plants

As is witnessed by railway statistics, level crossing (LC) safety has always been one of the major concerns for railway stakeholders. LC safety is an issue at the crossroads between technical aspects, operational procedures, and human factors, making the search for effective solutions a challenging task. This paper deals with technical aspects related to LC safety. In particular, we carry out an analysis pertaining to the diagnosability of two main failure classes that can affect the protection system at automatic LCs. In this paper, a labeled Petri net behavioral model depicting the global system function, including both the normal operation and the faulty behavior, is first established. Petri net has been used as the modeling formalism mainly for its mathematical foundations and expressiveness capabilities. Using such a mathematical notation is highly recommended to deal with dependability issues in safety-critical systems, particularly in railways. Based on the established model, different model-based approaches for the diagnosis of discrete event systems (DESs) will be brought into play to investigate the diagnosability of two considered failure classes, whereas the obtained results will be compared. In particular, a technique that we have established, which is based on on-the-fly and incremental analysis of the model state space, shows interesting efficiency, making it possible to tackle the combinatorial explosion problem, which arises particularly when considering multi-track LCs. The originality of this technique w.r.t. existing DES model-based diagnosis approaches is that, in general, a partial building/investigation of the state space suffices to decide diagnosability and build an online diagnoser. Findings pertaining to LC safety are drawn based on a thorough discussion of the obtained results. In particular, we show how the diagnosability analysis outputs can be taken into account in the global LC risk assessment process.

DxPCs: a toolbox for model-based diagnosis of dynamic systems using possible conflicts

Consistency-based diagnosis is a model-based diagnosis approach for the artificial intelligence community which relies upon models of correct behaviour and allows automatic multiple fault detection and isolation. The theory for static systems diagnosis is well established but there is a lack of free available tools implementing these ideas for dynamic systems, making rather difficult its dissemination. In this work we introduce DxPCs, a software tool capable of performing consistency-based diagnosis of continuous dynamic systems whose models can be represented as a set of algebraic differential equations. The diagnosis approach relies upon the possible conflict concept. DxPCs is able to automatically build the simulation models for each PC. Single-fault and multiple-fault scenarios, for both parametric and additive faults, can be injected, and studied. DxPCs allows the integration of different algorithms for fault detection, residual generation and evaluation, together with an incremental version of the minimal-hitting set algorithm for fault localization. The software architecture, together with performance results for one case study, are provided in this paper.

Online Monitoring and Fault Diagnosis of Hybrid Systems Using Switched Dynamic Bayesian Networks


 
 
 Modern real-world engineering systems typically have hybrid dynamic behaviors that can be modeled by continuous behaviors with discrete mode transitions. These complex systems present many significant challenges for online monitoring and diagnosis, including tracking system behavior, dealing with noisy measurements and disturbances, and diagnosing different types of faults. In this paper, we propose an integrated model-based diagnosis approach that extends the traditional Dynamic Bayesian Network-based particle filter approach for tracking continuous system dynamics. A novel mode diagnoser is presented that discriminates between residuals generated by inaccurate system tracking, discrete faults, and parametric faults. An extended quantitative fault isolation and identification scheme is combined with a qualitative fault isolation scheme to identify the abrupt parametric faults. We demonstrate the effectiveness of our approach by applying it to Reverse Osmosis (RO) subsystem of the Water Recovery System (WRS) developed at the NASA Johnson Space Center for long duration human missions.
 
 

SMT-Based Validation of Timed Failure Propagation Graphs

Timed Failure Propagation Graphs (TFPGs) are a formalism used in industry to describe failure propagation in a dynamic partially observable system. TFPGs are commonly used to perform model-based diagnosis. As in any model-based diagnosis approach, however, the quality of the diagnosis strongly depends on the quality of the model. Approaches to certify the quality of the TFPG are limited and mainly rely on testing. In this work we address this problem by leveraging efficient Satisfiability Modulo Theories (SMT) engines to perform exhaustive reasoning on TFPGs. We apply model-checking techniques to certify that a given TFPG satisfies (or not) a property of interest. Moreover, we discuss the problem of refinement and diagnosability testing and empirically show that our technique can be used to efficiently solve them.

Analysis of fault isolation assumptions when comparing model-based design approaches of diagnosis systems

Most model-based diagnosis approaches reported in the literature adopt a generic architecture and approach. However, the fault hypotheses generated by these methods may differ. This is not only due to the methods, but also on the basic assumptions made by different diagnostic algorithms on fault manifestation and evolution. While comparing different diagnosis approaches, the assumptions made in each case will have a significant effect on fault diagnosability performance and must therefore also be taken into consideration. Thus, to make a fair comparison, the different approaches should be designed based on the same assumptions. This paper studies the relation between a set of commonly made assumptions and fault isolability performance in order to compare different diagnosis approaches. As a case study, five developed diagnosis systems for a wind turbine benchmark problem are evaluated to analyze the type of assumptions that are applied in the different designs.

Model-based fault localization in bottling plants

The bottling of beverages is carried out in complex plants that consist of several machines and material flows. To realize an efficient bottling process and high quality products, operators try to avoid plant downtimes. With actual non-productive times of between 10% and 60%, the operators require diagnosis tools that allow them to locate plant components that cause downtime by exploiting automatically acquired machine data.This paper presents a model-based solution for automatic fault diagnosis in bottling plants. There are currently only a few plant-specific solutions (based on statistical calculations or artificial neural networks) for automatic bottling plant diagnosis. In order to develop a customizable solution, we followed the model-based diagnosis approach which allows the automatic generation of diagnosis solutions for individual plants. The existing stochastic and discrete-event models for bottling plants are not adequate for model-based diagnosis. Therefore, we developed new first-principle models for the relevant plant components, validated them numerically, and abstracted them to qualitative diagnosis models. Based on the diagnosis engine OCC’M Raz’r, application systems for two real plants and one virtual plant (based on discrete-event simulation) were generated and evaluated. Compared to the reasons for downtime identified by experts, we obtained up to 87.1% of compliant diagnosis results. The diagnosis solution was tested by practitioners and judged as a useful tool for plant optimization.

Computing minimal hitting set based on immune genetic algorithm

The primary bottleneck of many model-based diagnosis approaches is computing minimal hitting sets (MHSs). Most existing algorithms for computing MHSs are deterministic. Such algorithms are sound and complete but with the complexity of exponential, since the problem of computing MHSs is NP-hard. To reduce the complexity, especially for large systems, we present a novel approach for multiple fault diagnosis, based on an immune genetic algorithm (IGA). This approach maps a hitting set problem onto an integer programming problem, followed by obtaining the lower bound on the size of the solution. The use of the bound in the immune operator guides the MHS search. An elite-set strategy and an appropriate termination criterion are proposed to improve the efficiency of the approach. Experimental results show that our approach solves MHSs faster than the traditional deterministic algorithm in large-scale problems. Compared with the incomplete algorithm, the speed and accuracy of our approach are also significantly improved.

105 外力同定を援用したモデルベース診断手法の改良について : 近似的に測定データを増加させた場合の診断精度への影響(セッション2 同定技術)

105 外力同定を援用したモデルベース診断手法の改良について : 近似的に測定データを増加させた場合の診断精度への影響(セッション2 同定技術)