Diagnosis of an Alternator System Using Quantized Approaches

In this paper, the Generalized Cell Mapping (GCM) method for a linear system is compared with a new stochastic method for novel cell-to-cell mapping. The authors presented the new stochastic method in Mohon and Pisu (2013). The two methods are compared in an application example of a vehicle alternator. The alternator may experience three faults including belt slippage, a faulty diode connection, or incorrect controller reference voltage. Fault detection and isolation (FDI) is performed using the two cell-to-cell mapping methods. The results show that the new stochastic method is slower but yields better isolation results than the GCM method.

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Fault Detection and Isolation Based on Optimal Fault-Tolerant Observers for Linear System

2010 International Conference on Electrical and Control Engineering ◽

10.1109/icece.2010.249 ◽

2010 ◽

Cited By ~ 2

Author(s):

Jianbo Yi ◽

Qi Huang

Keyword(s):

Fault Detection ◽

Linear System ◽

Fault Tolerant ◽

Fault Detection And Isolation

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A Cell Mapping Method for Nonlinear Deterministic and Stochastic Systems—Part I: The Method of Analysis

Journal of Applied Mechanics ◽

10.1115/1.3171833 ◽

1986 ◽

Vol 53 (3) ◽

pp. 695-701 ◽

Cited By ~ 34

Author(s):

C. S. Hsu ◽

H. M. Chiu

Keyword(s):

Stochastic Systems ◽

Mapping Method ◽

Cell Mapping ◽

Generalized Cell Mapping ◽

Strongly Nonlinear ◽

Cell Mapping Methods ◽

The Past ◽

Simple Cell Mapping ◽

A Cell ◽

Vibration Problems

In the past few years as an attempt to devise more efficient and more practical ways of determining the global behavior of strongly nonlinear systems, two cell-to-cell mapping methods have been proposed, namely, the simple cell mapping and the generalized cell mapping. In this first part of the two-part paper we present a different and more efficient cell mapping method for treating nonlinear vibration problems. The vibratory systems may be deterministic or stochastic. The method utilizes compatible simple and generalized cell mapping and it combines the advantages of both. Applications to various systems will be presented in the second part of the paper.

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Event-triggered scheme for fault detection and isolation of non-linear system with time-varying delay

IET Control Theory and Applications ◽

10.1049/iet-cta.2018.5469 ◽

2020 ◽

Vol 14 (16) ◽

pp. 2429-2438

Author(s):

Muhammad Shamrooz Aslam ◽

Rizwan Ullah ◽

Xisheng Dai ◽

Andong Sheng

Keyword(s):

Fault Detection ◽

Linear System ◽

Fault Detection And Isolation ◽

Time Varying ◽

Time Varying Delay ◽

Non Linear ◽

Non Linear System ◽

Varying Delay ◽

Event Triggered

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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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