Nonlinear Fault Detection for Hydraulic Systems

One of the most important areas in the robotics industry is the development of robots capable of working in hazardous environments. As humans cannot safely or cheaply work in these environments, providing a high level of robotic functionality is important. Our work in this area focuses on a fault detection method known as analytical redundancy, or AR. In this paper we discuss the application to a hydraulic servovalve system of our novel rigorous nonlinear AR technique. AR is a model-based state-space technique that is theoretically guaranteed to derive the maximum number of independent tests of the consistency of sensor data with the system model and past control inputs. Conventional linear AR is only valid for linear sampled data systems. However, our new nonlinear AR (NLAR) technique maintains traditional linear AR’s mathematical guarantee to generate the maximum possible number of independent tests in the nonlinear domain. Thus NLAR allows us to gain the benefits of AR testing for nonlinear systems with both continuous and sampled data

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated

A STRUCTURAL APPROACH FOR THE DESIGN OF FAILURE DETECTION AND IDENTIFICATION SYSTEMS

International audiencethis paper presents a structural approach for the study of the monitoring ability on large scale systems. The structural canonical decomposition, obtained as a result of a structural study based on graph theory, points out the monitorable part of the system and provides a way to generate residuals. From this result, some extensions are possible as direct residual generation by variable elimination or sensor implementation

A STRUCTURAL APPROACH FOR THE DESIGN OF FAILURE DETECTION AND IDENTIFICATION SYSTEMS

International audiencethis paper presents a structural approach for the study of the monitoring ability on large scale systems. The structural canonical decomposition, obtained as a result of a structural study based on graph theory, points out the monitorable part of the system and provides a way to generate residuals. From this result, some extensions are possible as direct residual generation by variable elimination or sensor implementation

Fault tolerance of distributed systems by information pattern reconfiguration in the publisher/subscriber communication scheme

International audienc