Unsupervised Fault Detection in Refrigeration Showcase with Single Class Data using Autoencoders

2019 ◽  
Vol 139 (10) ◽  
pp. 1191-1200 ◽  
Author(s):  
Adamo Santana ◽  
Yu Kawamura ◽  
Kenya Murakami ◽  
Tatsuya Iizaka ◽  
Tetsuro Matsui ◽  
...  
Keyword(s):  
Fault Detection ◽  
Single Class

Automatic Fault Detection for Wind Turbines Using Single-Class Machine Learning Methods

2011 ◽  
Vol 58-60 ◽  
pp. 2602-2607
Author(s):  
Yi Hung Liu ◽  
Wei Zhi Lin ◽  
Jui Yiao Su ◽  
Yan Chen Liu
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Wind Turbine ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Learning Methods ◽  
Single Class ◽  
Machine Learning Methods

This work adopts data related to the rotor efficiency of wind turbine to estimate the performance of wind turbine. To achieve this goal, two novel machine learning methods are adopted to build models for wind-turbine fault detection: one is the support vector data description (SVDD) and the other is the kernel principal component analysis (KPCA). The data collected from a normally-operating wind turbine are used to train models. In addition, we also build a health index using the KPCA reconstruction error, which can be used to predict the performance of a wind turbine when it operates online. The data used in our experiments were collected from a real wind turbine in Taiwan. Experiments results show that the model based on KPCA performs better than the one based on SVDD. The highest fault detection rate for KPCA model is higher than 98%. The results also indicate the validity of using rotor efficacy to predict the overall performance of a wind turbine.

Modulation of Platelet Activation by Native DNA – Initial Description of Platelet Receptor Type, Number and Discrimination for Native DNA

1981 ◽  
Vol 45 (03) ◽  
pp. 263-266 ◽  
Author(s):  
B A Fiedel ◽  
M E Frenzke
Keyword(s):  
Platelet Aggregation ◽  
Human Platelets ◽  
Scatchard Analysis ◽  
Type Number ◽  
Single Class ◽  
Receptor Ligand ◽  
Platelet Receptor ◽  
Platelet Binding ◽  
Ligand Interactions ◽  
Initial Description

SummaryNative DNA (dsDNA) induces the aggregation of isolated human platelets. Using isotopically labeled dsDNA (125I-dsDNA) and Scatchard analysis, a single class of platelet receptor was detected with a KD = 190 pM and numbering ~275/platelet. This receptor was discriminatory in that heat denatured dsDNA, poly A, poly C, poly C · I and poly C · poly I failed to substantially inhibit either the platelet binding of, or platelet aggregation induced by, dsDNA; by themselves, these polynucleotides were ineffective as platelet agonists. However, poly G, poly I and poly G · I effectively and competitively inhibited platelet binding of the radioligand, independently activated the platelet and when used at a sub-activating concentration decreased the extent of dsDNA stimulated platelet aggregation. These data depict a receptor on human platelets for dsDNA and perhaps certain additional polynucleotides and relate receptor-ligand interactions to a physiologic platelet function.

Characterization of Thromboxane A2/Prostaglandin H2 Receptors in Porcine Coronary Artery -The Inhibitory Effect of a Novel Dibenzoxepin Derivative, KW-3635

1992 ◽  
Vol 67 (05) ◽  
pp. 582-584 ◽  
Author(s):  
Ichiro Miki ◽  
Akio Ishii
Keyword(s):  
Coronary Artery ◽  
Binding Sites ◽  
Thromboxane A2 ◽  
Inhibitory Effect ◽  
Scatchard Analysis ◽  
Single Class ◽  
Porcine Coronary Artery ◽  
Equilibrium Binding ◽  
H2 Receptors

SummaryWe characterized the thromboxane A2/prostaglandin H2 receptors in porcine coronary artery. The binding of [3H]SQ 29,548, a thromboxane A2 antagonist, to coronary arterial membranes was saturable and displaceable. Scatchard analysis of equilibrium binding showed a single class of high affinity binding sites with a dissociation constant of 18.5 ±1.0 nM and the maximum binding of 80.7 ± 5.2 fmol/mg protein. [3H]SQ 29,548 binding was concentration-dependently inhibited by thromboxane A2 antagonists such as SQ 29,548, BM13505 and BM13177 or the thromboxane A2 agonists such as U46619 and U44069. KW-3635, a novel dibenzoxepin derivative, concentration-dependently inhibited the [3H]SQ 29,548 binding to thromboxane A2/prosta-glandin H2 receptors in coronary artery with an inhibition constant of 6.0 ± 0.69 nM (mean ± S.E.M.).

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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