Fault Detection in Flexible Beams Based on Output Only Measurements

2020 ◽  
Author(s):  
Abdelrahman Khalil ◽  
Khaled F. Aljanaideh ◽  
Geoff Rideout ◽  
Mohammad Al Janaideh
Keyword(s):  
Fault Detection ◽  
Flexible Beams ◽  
Output Only

Path-Sensitive Oracle Data Selection via Static Analysis

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 110
Author(s):  
Mingzhe Zhang ◽  
Yunzhan Gong ◽  
Yawen Wang ◽  
Dahai Jin
Keyword(s):  
Fault Detection ◽  
Static Analysis ◽  
Detection Efficiency ◽  
Data Selection ◽  
Quality Analysis ◽  
Detection Capability ◽  
Test Oracles ◽  
Test Oracle ◽  
Efficiency And Effectiveness ◽  
Output Only

A test oracle is a procedure that is used during testing to determine whether software behaves correctly or not. One of most important tasks for a test oracle is to choose oracle data (the set of variables monitored during testing) to observe. However, most literature on test oracles has focused either on formal specification generation or on automated test oracle construction, whereas little work exists for supporting oracle data selection. In this paper, we present a path-sensitive approach, PSODS (path-sensitive oracle data selection), to automatically select oracle data for use by expected value oracles. PSODS ranks paths according to the possibility that potential faults may exist in them, and the ranked paths help testers determine which oracle data should be considered first. To select oracle data for each path, we introduce quantity and quality analysis of oracle data, which use static analysis to estimate oracle data for their substitution capability and fault-detection capability. Quantity analysis can reduce the number of oracle data. Quality analysis can rank oracle data based on their fault-detection capability. By using quantity and quality analysis, PSODS reduces the cost of oracle construction and improves fault-detection efficiency and effectiveness. We have implemented our approach and applied it to a real-world project. The experimental results show that PSODS is efficient in helping testers construct test oracles. Moreover, the oracle datasets produced by our approach are more effective and efficient than output-only oracles at detecting faults.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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.

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