Joint Distribution-Based Test Selection for Fault Detection and Isolation Under Multiple Faults Condition

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Yang Li ◽  
Enrico Zio ◽  
Ningyun Lu ◽  
Xiuli Wang ◽  
Bin Jiang
Keyword(s):  
Fault Detection ◽  
Joint Distribution ◽  
Fault Detection And Isolation ◽  
Test Selection ◽  
Multiple Faults ◽  
Selection For

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.

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.

Performance Analysis of Fault Detection and Identification for Multiple Faults in GNSS and GNSS/INS Integration

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Muwaffaq Alqurashi ◽  
Jinling Wang
Keyword(s):  
Performance Analysis ◽  
Fault Detection ◽  
Real Time ◽  
Correlation Coefficients ◽  
Geometric Constraints ◽  
Fault Detection And Isolation ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Detection And Identification ◽  
Fault Detection And Identification

AbstractFor positioning, navigation and timing (PNT) purposes, GNSS or GNSS/INS integration is utilised to provide real-time solutions. However, any potential sensor failures or faulty measurements due to malfunctions of sensor components or harsh operating environments may cause unsatisfactory estimation for PNT parameters. The inability for immediate detecting faulty measurements or sensor component failures will reduce the overall performance of the system. So, real time detection and identification of faulty measurements is required to make the system more accurate and reliable for different applications that need real time solutions such as real time mapping for safety or emergency purposes. Consequently, it is necessary to implement an online fault detection and isolation (FDI) algorithm which is a statistic-based approach to detect and identify multiple faults.However, further investigations on the performance of the FDI for multiple fault scenarios is still required. In this paper, the performance of the FDI method under multiple fault scenarios is evaluated, e.g., for two, three and four faults in the GNSS and GNSS/INS measurements under different conditions of visible satellites and satellites geometry. Besides, the reliability (e.g., MDB) and separability (correlation coefficients between faults detection statistics) measures are also investigated to measure the capability of the FDI method. A performance analysis of the FDI method is conducted under the geometric constraints, to show the importance of the FDI method in terms of fault detectability and separability for robust positioning and navigation for real time applications.

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 Fault Detection, Isolation, and Accommodation for LTI Systems Based on GIMC Structure

2008 ◽  
Vol 2008 ◽  
pp. 1-15
Author(s):  
D. U. Campos-Delgado ◽  
E. Palacios ◽  
D. R. Espinoza-Trejo
Keyword(s):  
Fault Detection ◽  
Model Uncertainty ◽  
Fault Tolerant ◽  
Internal Model Control ◽  
Fault Detection And Isolation ◽  
Design Strategies ◽  
Multiple Faults ◽  
Simulation Evaluation ◽  
Model Control ◽  
Nominal Condition

In this contribution, an active fault-tolerant scheme that achieves fault detection, isolation, and accommodation is developed for LTI systems. Faults and perturbations are considered as additive signals that modify the state or output equations. The accommodation scheme is based on the generalized internal model control architecture recently proposed for fault-tolerant control. In order to improve the performance after a fault, the compensation is considered in two steps according with a fault detection and isolation algorithm. After a fault scenario is detected, a general fault compensator is activated. Finally, once the fault is isolated, a specific compensator is introduced. In this setup, multiple faults could be treated simultaneously since their effect is additive. Design strategies for a nominal condition and under model uncertainty are presented in the paper. In addition, performance indices are also introduced to evaluate the resulting fault-tolerant scheme for detection, isolation, and accommodation. Hard thresholds are suggested for detection and isolation purposes, meanwhile, adaptive ones are considered under model uncertainty to reduce the conservativeness. A complete simulation evaluation is carried out for a DC motor setup.

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