scholarly journals Application of a Fault Detection and Isolation System on a Rotary Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Silvia M. Zanoli ◽  
Giacomo Astolfi
Keyword(s):  
Fault Detection ◽  
Centrifugal Compressor ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Isolation System ◽  
Multiple Faults ◽  
Rotary Machine ◽  
Model Free ◽  
Refinery Plant

The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI) system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA), has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved.

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

TAPPI Journal ◽  
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.

Image-Based Versus Signal-Based Sensors for Machine Fault Detection and Isolation

2014 ◽  
Author(s):  
Heshan Fernando ◽  
Vedang Chauhan ◽  
Brian Surgenor
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Fault Isolation ◽  
Video Data ◽  
Fault Detection And Isolation ◽  
Single Camera ◽  
Multiple Faults ◽  
Multiple Sensors ◽  
Machine Fault ◽  
And Performance

This paper presents the results of a comparative study that investigated the use of image-based and signal-based sensors for fault detection and fault isolation of visually-cued faults on an automated assembly machine. The machine assembles 8 mm circular parts, from a bulk-supply, onto continuously moving carriers at a rate of over 100 assemblies per minute. Common faults on the machine include part jams and ejected parts that occur at different locations on the machine. Two sensor systems are installed on the machine for detecting and isolating these faults: an image-based system consisting of a single camera and a signal-based sensor system consisting of multiple greyscale sensors and limit switches. The requirements and performance of both systems are compared for detecting six faults on the assembly machine. It is found that both methods are able to effectively detect the faults but they differ greatly in terms of cost, ease of implementation, detection time and fault isolation capability. The conventional signal-based sensors are low in cost, simple to implement and require little computing power, but the installation is intrusive to the machine and readings from multiple sensors are required for faster fault detection and isolation. The more sophisticated image-based system requires an expensive, high-resolution, high-speed camera and significantly more processing power to detect the same faults; however, the system is not intrusive to the machine, fault isolation becomes a simpler problem with video data, and the single camera is able to detect multiple faults in its field of view.

Fault detection and isolation in nonlinear systems with partial Reduced Kernel Principal Component Analysis method

2016 ◽  
Vol 40 (4) ◽  
pp. 1289-1296 ◽  
Author(s):  
Ines Jaffel ◽  
Okba Taouali ◽  
Mohamed Faouzi Harkat ◽  
Hassani Messaoud
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Kernel Principal Component Analysis ◽  
Continuous Stirred Tank Reactor ◽  
Analysis Method ◽  
Continuous Stirred Tank

In this article, we suggest an extension of our proposed method in fault detection called Reduced Kernel Principal Component Analysis (RKPCA) (Taouali et al., 2015) to fault isolation. To this end, a set of structured residues is generated by using a partial RKPCA model. Furthermore, each partial RKPCA model was performed on a subset of variables to generate structured residues according to a properly designed incidence matrix. The relevance of the proposed algorithm is revealed on Continuous Stirred Tank Reactor.

scholarly journals Fault Detection and Isolation with Robust Principal Component Analysis

2008 ◽  
Vol 18 (4) ◽  
pp. 429-442 ◽  
Author(s):  
Yvon Tharrault ◽  
Gilles Mourot ◽  
José Ragot ◽  
Didier Maquin
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Covariance Matrix ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Isolation ◽  
Robust Principal Component Analysis ◽  
Sample Mean ◽  
Simple Estimate ◽  
Multiple Faults

Fault Detection and Isolation with Robust Principal Component AnalysisPrincipal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a "local" matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.

scholarly journals Smac–Fdi: A Single Model Active Fault Detection and Isolation System for Unmanned Aircraft

2015 ◽  
Vol 25 (1) ◽  
pp. 189-201 ◽  
Author(s):  
Guillaume J.J. Ducard
Keyword(s):  
Fault Detection ◽  
Active Fault ◽  
Parameter Tuning ◽  
Fault Isolation ◽  
Unmanned Aircraft ◽  
Fault Detection And Isolation ◽  
Actuator Fault ◽  
Single Model ◽  
Isolation System ◽  
Active Fault Detection

Abstract This article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers.

scholarly journals Fault Detection and Isolation in Inertial Measurement Units Based on -CUSUM and Wavelet Packet

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Élcio Jeronimo de Oliveira ◽  
Ijar Milagre da Fonseca ◽  
Hélio Koiti Kuga
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Median Filter ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Measurement Unit ◽  
Short Delay ◽  
Inertial Measurement ◽  
Low Level

The aim of this paper is to present a fault detection algorithm (FDI) based on signal processing techniques developed for an inertial measurement unit (IMU) with minimal redundancy of fiber optic gyros. In this work the recursive median filter is applied in order to remove impulses (outliers) arising from data acquisition process and parity vector operations, improving the fault detection and isolation performance. The FDI algorithm is divided into two blocks: fault detection (FD) and fault isolation (FI). The FD part of the algorithm is used to guarantee the reliability of the isolation part and is based on parity vector analysis using -CUSUM algorithm. The FI part is performed using parity space projection of the energy subbands obtained from wavelet packet decomposition. This projection is an extension of clustering analysis based on singular value decomposition (SVD) and principal component analysis (PCA). The results of the FD and FI algorithms have shown the effectiveness of the proposed method, in which the FD algorithm is capable of indicating the low-level step bias fault with short delay and a high index of correct decisions of the FI algorithm also with low-level step bias fault.

scholarly journals Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shulan Kong ◽  
Mehrdad Saif ◽  
Guozeng Cui
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fractional Order ◽  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Fault Isolation ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation ◽  
Order System

This study investigates estimation and fault diagnosis of fractional-order Lithium-ion battery system. Two simple and common types of observers are designed to address the design of fault diagnosis and estimation for the fractional-order systems. Fractional-order Luenberger observers are employed to generate residuals which are then used to investigate the feasibility of model based fault detection and isolation. Once a fault is detected and isolated, a fractional-order sliding mode observer is constructed to provide an estimate of the isolated fault. The paper presents some theoretical results for designing stable observers and fault estimators. In particular, the notion of stability in the sense of Mittag-Leffler is first introduced to discuss the state estimation error dynamics. Overall, the design of the Luenberger observer as well as the sliding mode observer can accomplish fault detection, fault isolation, and estimation. The effectiveness of the proposed strategy on a three-cell battery string system is demonstrated.

scholarly journals Overview of fault detection methods for power plant control systems purposes

2019 ◽  
pp. 127-129
Author(s):  
Dmytro Shram ◽  
Oleksandr Stepanets
Keyword(s):  
Fault Detection ◽  
Control Systems ◽  
Power Plants ◽  
Fault Detection And Isolation ◽  
Data Driven ◽  
Detection Methods ◽  
Model Free ◽  
Power Plant Control ◽  
Technical Details ◽  
Plant Control

The main objective of this paper is to review of fault detection and isolation (FDI) methods and applications on various power plants. Due to the focus of the topic, on model and model-free FDI methods, technical details were kept in the references. We will overview the methods in terms of model-based, data driven and signal based methods further in the paper. Principles of three FDI methods are explained and characteristics of number of some popular techniques are described. It also summarizes data-driven methods and applications related to power generation plants. Parts of control system applications of FDI in TPPs with possible faults are shown in the Table I. Some popular techniques for the various faults in TPPs are discussed also.

Sign in / Sign up

Export Citation Format

Share Document