An optimized knight traversal technique to detect multiple faults and Module Sequence Graph based reconfiguration of microfluidic biochip

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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Cascading Failures Assessment in Renewable Integrated Power Grids Under Multiple Faults Contingencies

IEEE Access ◽

10.1109/access.2021.3087195 ◽

2021 ◽

pp. 1-1

Author(s):

Muhammad Adnan ◽

Muhammad Gufran Khan ◽

Arslan Ahmed Amin ◽

Muhammad Rayyan Fazal ◽

Wen Shan Tan ◽

...

Keyword(s):

Power Grids ◽

Cascading Failures ◽

Multiple Faults

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Fault detection and diagnosis for multiple faults of VAV terminals using self-adaptive model and layered random forest

Building and Environment ◽

10.1016/j.buildenv.2021.107667 ◽

2021 ◽

Vol 193 ◽

pp. 107667

Author(s):

Haitao Wang ◽

Daoguang Feng ◽

Kai Liu

Keyword(s):

Random Forest ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Adaptive Model ◽

Multiple Faults ◽

Detection And Diagnosis ◽

Self Adaptive

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A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

Sensors ◽

10.3390/s21092922 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2922

Author(s):

Fan Zhang ◽

Ye Wang ◽

Yanbin Gao

Keyword(s):

Fault Detection ◽

Parametric Bootstrap ◽

Integrated System ◽

Range Data ◽

Navigation Systems ◽

Integrated Navigation ◽

Multiple Faults ◽

Detection And Identification ◽

Fault Detection And Identification ◽

Tightly Coupled

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

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Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation

Proceedings of the 29th ACM International Conference on Information & Knowledge Management ◽

10.1145/3340531.3411939 ◽

2020 ◽

Author(s):

Jingyue Gao ◽

Yang Lin ◽

Yasha Wang ◽

Xiting Wang ◽

Zhao Yang ◽

...

Keyword(s):

Sequence Graph

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A hybrid hidden Markov model towards fault detection of rotating components

Journal of Vibration and Control ◽

10.1177/1077546315627934 ◽

2016 ◽

Vol 23 (19) ◽

pp. 3175-3195 ◽

Cited By ~ 8

Author(s):

Ayan Sadhu ◽

Guru Prakash ◽

Sriram Narasimhan

Keyword(s):

Fault Detection ◽

Markov Model ◽

Hidden Markov Model ◽

Time Scale ◽

Hidden Markov ◽

Energy Operator ◽

Fault Classification ◽

Multiple Faults ◽

Condition Indicators ◽

Model Based

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

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

Volume 3: Engineering Systems; Heat Transfer and Thermal Engineering; Materials and Tribology; Mechatronics; Robotics ◽

10.1115/esda2014-20102 ◽

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.

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