Shunsuke Chigusa

Model-Based Prognostic Techniques Applied to a Suspension System

Conventional maintenance strategies, such as corrective and preventive maintenance, are not adequate to fulfill the needs of expensive and high availability transportation and industrial systems. A new strategy based on forecasting system degradation through a prognostic process is required. The recent advances in model-based design technology have realized significant time savings in product development cycle. These advances facilitate the integration of model-based diagnosis and prognosis of systems, leading to condition-based maintenance and increased availability of systems. With an accurate simulation model of a system, diagnostics and prognostics can be synthesized concurrently with system design. In this paper, we develop an integrated prognostic process based on data collected from model-based simulations under nominal and degraded conditions. Prognostic models are constructed based on different random load conditions (modes). An interacting multiple model (IMM) is used to track the hidden damage. Remaining-life prediction is performed by mixing mode-based life predictions via time-averaged mode probabilities. The solution has the potential to be applicable to a variety of systems, ranging from automobiles to aerospace systems.

An interacting multiple model approach to model-based prognostics

A system wide prognostic process is required to fulfill the needs of expensive and high availability industrial systems. The recent advances in model-based design technology have facilitated the integration of model-based diagnosis and prognosis of systems, leading to condition-based maintenance. In this paper an integrated prognostic process based on data collected from model-based simulations under nominal and degraded conditions is described. Interacting Multiple Model (IMM) is used to track the hidden damage. Remaining life prediction is performed by mixing mode-based life predictions via time-averaged mode probabilities. The prognostic process is demonstrated on a suspension system.

An Integrated Diagnostic Development Process for Automotive Engine Control Systems

Theory and applications of model-based fault diagnosis have progressed significantly in the last four decades. In addition, there has been increased use of model-based design and testing in the automotive industry to reduce design errors, perform rapid prototyping, and hardware-in-the-loop simulation (HILS). This paper presents a new model-based diagnostic development process for automotive engine control systems. This process seamlessly employs a graph-based dependency model and mathematical models for online/offline diagnosis. The hybrid method improves the diagnostic systems accuracy and consistency, utilizes existing validated knowledge on empirical models, enables remote diagnosis, and responds to the challenges of increased system complexity. The development platform consists of an engine electronic control unit (ECU) rapid prototyping system and HILS equipment - the air intake subsystem (AIS). The diagnostic strategy is tested and validated using the HILS platform.

Integrated Model-Based and Data-Driven Diagnosis of Automotive Antilock Braking Systems

Model-based fault diagnosis, using statistical hypothesis testing, residual generation (by analytical redundancy), and parameter estimation, has been an active area of research for the past four decades. However, these techniques are developed in isolation, and generally, a single technique cannot address the diagnostic problems in complex systems. In this paper, we investigate a hybrid approach, which combines model-based and data-driven techniques to obtain better diagnostic performance than the use of a single technique alone, and demonstrate it on an antilock braking system. In this approach, we first combine the parity equations and a nonlinear observer to generate the residuals. Statistical tests, particularly the generalized likelihood ratio tests, are used to detect and isolate a subset of faults that are easier to detect. Support vector machines are used for fault isolation of less-sensitive parametric faults. Finally, subset selection (via fault detection and isolation) is used to accurately estimate fault severity.

Diagnosis knowledge representation and inference

In this article, we presented three graphical modeling techniques for diagnostic knowledge representation and inference: behavioral Petri nets (BPNs), multisignal flow graphs, and Bayesian networks (BNs). By using the same example from (Portinale, 1997) we showed that both multisignal flow graph model and BN model yield the same diagnosis. In addition, we showed that the P-invariant concept in BPN is similar to the D-separation concept in BNs

Data Reduction Techniques for Intelligent Fault Diagnosis in Automotive Systems

Faults in automotive systems significantly degrade the performance and efficiency of vehicles, and often times are the major causes of vehicle break-down leading to large expenditure for repair and maintenance. An intelligent fault diagnosis system can ensure uninterrupted and reliable operation of vehicular systems, and aid in vehicle health monitoring. Due to cost constraints, the current electronic control units (ECUs) for control and diagnosis have 1-2 MB of memory and 24 -50 MHz of processor speed. Therefore, intelligent data reduction techniques and partitioning methodology are needed for effective fault diagnosis in automotive systems. In this paper, we propose a data- driven approach using a data reduction technique, coupled with a variety of classifiers, for an automotive engine system. Adaptive boosting (AdaBoost) is employed to improve the classifier performance. Our proposed techniques can be used for any vehicle systems without the need to tune the classification algorithms for a specific vehicle model. Our proposed fault diagnosis scheme results in significant reduction in data size (25.6 MBrarr12.8 KB) without loss of accuracy in classification.

Agent-based real-time fault diagnosis

Theory and applications of model-based fault diagnosis have progressed significantly in the last four decades. In addition, there has been increasing use of model-based design and testing in automotive industry to reduce design errors, perform real-time simulations for rapid prototyping, and hardware-in-the-loop testing. For vehicle diagnosis, a global diagnosis method, which collects the diagnostic information from all the subsystem electronic control units (ECUs), is not practical because of high communication requirements and time delays induced by centralized diagnosis. Consequently, an agent-based distributed diagnosis architecture is needed. In this architecture, each subsystem resident agent (embedded in the ECU) performs its own fault inference and communicate the diagnostic results to a vehicle expert agent. A vehicle expert agent performs cross-subsystem diagnosis to resolve conflicts among resident agents, and to provide an accurate vehicle-level diagnostic inference. In this paper, we propose a systematic way to design an agent-based diagnosis architecture. A hybrid model-based technique that seamlessly employs a graph-based dependency model and quantitative models for intelligent diagnosis is applied to each individual ECU. Diagnostic tests for each individual ECU are designed via model-based diagnostic techniques based on a quantitative model. The fault simulation results, in the form of a diagnostic matrix, are extracted into a dependency model for fast fault inference by a resident agent. The global diagnostic inference is performed through a vehicle expert agent that trades off computational complexity and communication load. This architecture is demonstrated on the engine air induction subsystem. The solution is generic and can be applied to a variety of distributed control systems

An Optimization-Based Method for Dynamic Multiple Fault Diagnosis Problem

Imperfect test outcomes, due to factors such as unreliable sensors, electromagnetic interference, and environmental conditions, manifest themselves as missed detections and false alarms. The main objective of our research on on-board diagnostic inference is to develop near-optimal algorithms for dynamic multiple fault diagnosis (DMFD) problems in the presence of imperfect test outcomes. Our problem is to determine the most likely evolution of fault states, the one that best explains the observed test outcomes. Here, we develop a primal-dual algorithm for solving the DMFD problem by combining Lagrangian relaxation and the Viterbi decoding algorithm in an iterative way. A novel feature of our approach is that the approximate duality gap provides a measure of suboptimality of the DMFD solution.

Dynamic fusion of classifiers for fault diagnosis

This paper considers the problem of temporally fusing classifier outputs to improve the overall diagnostic classification accuracy in safety-critical systems. Here, we discuss dynamic fusion of classifiers which is a special case of the dynamic multiple fault diagnosis (DMFD) problem [1]-[3]. The DMFD problem is formulated as a maximum a posteriori (MAP) configuration problem in tri-partite graphical models, which is NP-hard. A primal-dual optimization framework is applied to solve the MAP problem. Our process for dynamic fusion consists of four key steps: (1) data preprocessing such as noise suppression, data reduction and feature selection using data-driven techniques, (2) error correcting codes to transform the multiclass data into binary classification, (3) fault detection using pattern recognition techniques (support vector machines in this paper), and (4) dynamic fusion of classifiers output labels over time using the DMFD algorithm. An automobile engine data set, simulated under various fault conditions [4], was used to illustrate the fusion process. The results demonstrate that an ensemble of classifiers, when fused over time, reduces the classification error as compared to a single classifier and static fusion of classifiers trained over the entire batch of data. The results for sliding window dynamic fusion are also provided.

Integrated model-based and data-driven diagnostic strategies applied to an anti-lock brake system

Model-based fault diagnosis, using statistical techniques, residual generation (by analytical redundancy), and parameter estimation, has been an active area of research for the past four decades. However, these techniques are developed in isolation and generally a single technique can not address the diagnostic problems in complex systems. In this paper, we investigate a hybrid approach, which combines different techniques to obtain better diagnostic performance than the use of a single technique alone, and demonstrate it on an anti-lock brake system. In this approach, we first combine the parity equations and nonlinear observer to generate the residuals. Statistical tests, in particular generalized likelihood ratio tests (GLRT), are used to detect a subset of faults that are easier to detect. Support vector machines (SVM) is used for fault isolation of less-sensitive parametric faults. Finally, subset selection for improved parameter estimation is used to estimate fault severity