Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Abstract

Malfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.

Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems

“…Based on this line of reasoning, it can be argued that system faults, or the occurrence of abnormal events affects the frequency of occurrence and duration time of SOMs, making possible their use as fault indicators. This phenomenon has already been reported in [28], where the concept of failure-induced operation modes (FIOM) was introduced. This concept states that certain faults may lead to the occurrence of new SOMs that are not common in fault-free operation.…”

“…The proposed heath management and failure prognosis method evaluates variations in Fq and D in order to detect faults, and estimating the system's time to failure. It is claimed that, a particular failure mode tends to affect the same set of variables every time it occurs [28]. As a consequence, the system manipulates the same set of actuators as compensation, leading to similar variations in Fq and D, whenever the failure mode is repeated.…”

“…Actuator signals S A are binary signals (0 or 1), where 0 represents the inactive state of a particular component and 1 is the active state [28]. Table 1 presents a description of the actuators installed in the greenhouse testbed.…”

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

“…On the other hand, certain SOMs did not occur due to the effect of failures, or specific SOMs emerged that did not occur during regular system operation. Consequently, the main contribution of this work was providing novel means, such as the concept of failure induced operation modes, for monitoring changes of states, events and situations of a quasi-dynamic system [80]. The changes were captured through the observed variations in the frequency and the duration of SOMs-that is, in the system dynamics rather than only by signal deviations.…”

Facilitating Autonomous Systems with AI-Based Fault Tolerance and Computational Resource Economy

“…Passive FTC type requires a prior knowledge of the faults, while the Active type (used in this work) does not necessitate such knowledge of the fault rather a Fault Detection and Isolation (FDI) mechanism with reconfigurable control. Reconfigurable FTC control has gained significant attention over recent years given the demand on reliable system design [20][21][22] and in the area of cyber-physical systems [23].…”