Improving decision making in fault detection and isolation using model validity

Abstract Reliability and safety aspects are becoming much more important due to higher quality requirements, complicated and/or connected processes. The fault monitoring systems to be commonly used in machine- and rotordynamics are based on signal analysis methods. Furthermore, various kinds of fault detection and isolation (FDI)-schemes are already applied to a lot of technical applications of detecting and isolating sensor and actuator failures (Isermann, 1994; van Schrick, 1994) and also to fault detection in power plants (in general) or in manufacturing machines. An implicit assumption is that process or machine changes due to faults lead to changes in calculated parameters, which are unique and unambiguous. In the case of applying methods of signal analysis this means spectrums etc. the vibration behaviour will be monitored very well but have to be interpreted. On the other hand signal parameters usually only describe the system by analyzing output signals without use of known and unknown inner parameters and/or inputs. These parameters are available, and normally this knowledge is used by the operating staff interpreting the resulting signal parameters. In this way a decision-making problem appears so that questions about the physical character of faults, about the existence of special faults and also about the location of failures/faults has to be answered. In this way the experience and knowledge of the interpreting persons are very important. In this contribution the problems of the decision-making process are tried to defuse: • The available knowledge about the unfaulty system parameters is used to built up beside a nominal system model an unambiguous fault-specific ratio. Inner states of the structure are estimated by an PI-observer. • The developed robust PI-observer (Söffker et al., 1993a; Söffker et al., 1995a) estimates inner states and unknown inputs. In (Söffker et al., 1993b) this new method is applied to the crack detection of a rotor, but not proved. In this paper the proof is given and a generalization is described. The advantages in contrast to usual signal based vibration monitoring systems and also modern FDI-schemes are shown.

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A case-based reasoning system for fault detection and isolation: a case study on complex gearboxes

Journal of Quality in Maintenance Engineering ◽

10.1108/jqme-05-2018-0039 ◽

2019 ◽

Vol 25 (2) ◽

pp. 213-235 ◽

Cited By ~ 4

Author(s):

Soumava Boral ◽

Sanjay Kumar Chaturvedi ◽

V.N.A. Naikan

Keyword(s):

Decision Making ◽

Fault Detection ◽

Large Scale ◽

Fault Detection And Isolation ◽

Decision Making Process ◽

Case Based Reasoning ◽

Content Type ◽

Complex Decision Making ◽

Complex Decision ◽

Case Based

Purpose Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar. Design/methodology/approach CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI). Findings The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers. Originality/value The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

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Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽

10.32964/tj13.1.33 ◽

2014 ◽

Vol 13 (1) ◽

pp. 33-41

Author(s):

YVON THARRAULT ◽

MOULOUD AMAZOUZ

Keyword(s):

Fault Detection ◽

Early Detection ◽

False Alarm ◽

False Alarm Rate ◽

Principal Component ◽

Fault Isolation ◽

Fault Detection And Isolation ◽

Detection Scheme ◽

Multiple Faults ◽

Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

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