Model-based fault diagnosis in continuous dynamic systems

Fault Detection of continuous time linear switched systems using combination of Bond Graph method and switching observer

In this paper, a new Fault Detection (FD) scheme based on combination of switching observer and Bond Graph (BG) method for linear continuous time switched systems with Average Dwell Time (ADT) approach is proposed. The proposed scheme is a BG-based two-stage FD system in which the compact state space representation and Global Analytical Redundancy Relations (GARRs) are derived based on the BG model. In the first stage, a switched observer is designed considering disturbance attenuation level, fault sensitivity and rapid fault detection criteria by solving a weighted Linear Matrix Inequality (LMI) optimization problem. Next, a new form of GARRs which is based on output estimation error of the observer and is called Error-based Global Analytical Redundancy Relations (EGARRs) is developed to combine the observer and BG method. The output estimation errors from the observer, which are adequately sensitive to faults and simultaneously the effects of disturbances are attenuated therein, are given to the EGARRs to generate the residuals of the FD system. The proposed method may be used for fault detection of switched linear systems with ADT based on the BG model of the system. Finally, two case studies including a two-tank system and a buck converter-driven DC motor are considered to show the efficiency and real-time implementation of the proposed method.

Sensor fault diagnosis based on energy balance evaluation: application to a metal processing

This paper deals with the design of a residual generator for fault detection and isolation in the dynamic closed-loop systems based on the balance of energy which "enters" and "leaves" plants. The main contribution of this paper consists in developing a suitable fault detection and isolation technique to detect faults in single-input single-output closed-loop system based on major signals without the requirement of an accurate static or dynamic model. Indeed, in the absence of conventional input-output models, the proposed method involves the on-line energy balance evaluation to detect a sensor fault. The application to the monitoring of a galvanizing line in steel industry shows the effectiveness of the suggested approach when a sensor fault occurs.

Understanding health care organization needs and context. Beyond performance gaps

Significant efforts have been invested in improving our understanding of how to accelerate and magnify the impact of research on clinical practice. While approaches to fostering translation of research into practice are numerous, none appears to be superior and the evidence for their effectiveness is mixed. Lessons learned from formative evaluation have given us a greater appreciation of the contribution of context to successful implementation of quality improvement interventions. While formative evaluation is a powerful tool for addressing context effects during implementation, lessons learned from the social sciences (including management and operations research, sociology, and public health) show us that there are also powerful preimplementation tools available to us. This paper discusses how we might integrate these tools into implementation research. We provide a theoretical framework for our need to understand organizational contexts and how organizational characteristics can alert us to situations where preimplementation tools will prove most valuable.