Multiple Fault Detection and Isolation Using the Haar Transform, Part 1: Theory

1999 ◽  
Vol 121 (2) ◽  
pp. 290-294 ◽  
Author(s):  
C. K. H. Koh ◽  
J. Shi ◽  
W. J. Williams ◽  
J. Ni
Keyword(s):  
Low Cost ◽  
A Priori ◽  
Fault Detection And Isolation ◽  
Ratio Test ◽  
Process Variables ◽  
Detection Rates ◽  
Haar Transform ◽  
Priori Information ◽  
Exclusive Sets ◽  
Process Measurements

Most manufacturing processes involve several process variables which interact with one another to produce a resultant action on the part. A fault is said to occur when any of these process variables deviate beyond their specified limits. An alarm is triggered when this happens. Low cost and less sophisticated detection schemes based on threshold bounds on the original measurements (without feature extraction) often suffer from high false alarm and missed detection rates when the process measurements are not properly conditioned. They are unable to detect frequency or phase shifted fault signals whose amplitudes remain within specifications. They also provide little or no information about the multiplicity (number of faults in the same process cycle) or location (the portion of the cycle where the fault was detected) of the fault condition. A method of overcoming these limitations is proposed in this paper. The Haar transform is used to generate sets of detection signals from the original measurements of process monitoring signals. By partitioning these signals into disjoint segments, mutually exclusive sets of Haar coefficients can be used to locate faults at different phases of the process. The lack of a priori information on fault condition is overcomed by using the Neyman-Pearson criteria for the uniformly most powerful form (UMP) of the likelihood ratio test (LRT).

Multiple Fault Detection and Isolation Using the Haar Transform, Part 2: Application to the Stamping Process

1999 ◽  
Vol 121 (2) ◽  
pp. 295-299 ◽  
Author(s):  
C. K. H. Koh ◽  
J. Shi ◽  
W. J. Williams ◽  
J. Ni
Keyword(s):  
Sheet Metal ◽  
Fault Detection And Isolation ◽  
Strain Gages ◽  
Process Variables ◽  
Multiple Fault ◽  
The Press ◽  
Exclusive Sets ◽  
Detection Schemes ◽  
Current Detection

The sheet metal drawing operation is a complex manufacturing process involving more than forty process variables. The intricate interaction among these variables affect the forming tonnage which is measured by strain gages mounted on the press. A fault is said to occur when any of these process variables deviate beyond their specified limits. Current detection schemes based on thresholding do not fully exploit the information in the tonnage signals for the detection and isolation of multiple fault condition. It is thus an excellent case study for demonstrating the implementation of the detection methodology presented in Part 1. By partitioning the tonnage signature into disjoint segments, mutually exclusive sets of Haar coefficients can be used to isolate faults in each stage of the process.

An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks

2017 ◽  
Vol 27 (03) ◽  
pp. 1650047 ◽  
Author(s):  
Manuel Roveri ◽  
Francesco Trovò
Keyword(s):  
Sensor Networks ◽  
Fault Detection ◽  
A Priori ◽  
Fault Detection And Diagnosis ◽  
Fault Detection And Isolation ◽  
Isolation System ◽  
Temporal Relationships ◽  
Data Generating Process ◽  
Detection And Diagnosis ◽  
Priori Information

Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets.

Innovative Approach to Information Search by Example of a Patent Analysis of an Important Substitution Plan

2020 ◽  
pp. 143-157
Author(s):  
Maria A. Milkova
Keyword(s):  
Information Search ◽  
Topic Modeling ◽  
Cognitive Biases ◽  
A Priori ◽  
Import Substitution ◽  
Innovative Approach ◽  
Iterative Search ◽  
Comprehensive Picture ◽  
Priori Information ◽  
Selection Of

Nowadays the process of information accumulation is so rapid that the concept of the usual iterative search requires revision. Being in the world of oversaturated information in order to comprehensively cover and analyze the problem under study, it is necessary to make high demands on the search methods. An innovative approach to search should flexibly take into account the large amount of already accumulated knowledge and a priori requirements for results. The results, in turn, should immediately provide a roadmap of the direction being studied with the possibility of as much detail as possible. The approach to search based on topic modeling, the so-called topic search, allows you to take into account all these requirements and thereby streamline the nature of working with information, increase the efficiency of knowledge production, avoid cognitive biases in the perception of information, which is important both on micro and macro level. In order to demonstrate an example of applying topic search, the article considers the task of analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1,500 products and technologies for the proposed import substitution. The use of patent search based on topic modeling allows to search immediately by the blocks of a priori information – terms of industrial plans for import substitution and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to visually obtain more detailed information about which groups of products and technologies have been patented.

scholarly journals Computational Method for Wavefront Sensing Based on Transport-Of-Intensity Equation

Photonics ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 177
Author(s):  
Iliya Gritsenko ◽  
Michael Kovalev ◽  
George Krasin ◽  
Matvey Konoplyov ◽  
Nikita Stsepuro
Keyword(s):  
A Priori ◽  
Spherical Wave ◽  
Computational Method ◽  
Optical Systems ◽  
Radius Of Curvature ◽  
Imaging Method ◽  
Wavefront Sensing ◽  
Phase Imaging ◽  
Transport Of Intensity Equation ◽  
Priori Information

Recently the transport-of-intensity equation as a phase imaging method turned out as an effective microscopy method that does not require the use of high-resolution optical systems and a priori information about the object. In this paper we propose a mathematical model that adapts the transport-of-intensity equation for the purpose of wavefront sensing of the given light wave. The analysis of the influence of the longitudinal displacement z and the step between intensity distributions measurements on the error in determining the wavefront radius of curvature of a spherical wave is carried out. The proposed method is compared with the traditional Shack–Hartmann method and the method based on computer-generated Fourier holograms. Numerical simulation showed that the proposed method allows measurement of the wavefront radius of curvature with radius of 40 mm and with accuracy of ~200 μm.

scholarly journals Sensorimotor priors in nonstationary environments

2013 ◽  
Vol 109 (5) ◽  
pp. 1259-1267 ◽  
Author(s):  
Devika Narain ◽  
Robert J. van Beers ◽  
Jeroen B. J. Smeets ◽  
Eli Brenner
Keyword(s):  
Environmental Variables ◽  
A Priori ◽  
Past Research ◽  
Systematic Change ◽  
Recent Experience ◽  
Environmental Variance ◽  
Adaptive Models ◽  
Priori Information ◽  
Human Nervous System ◽  
Better Than

In the course of its interaction with the world, the human nervous system must constantly estimate various variables in the surrounding environment. Past research indicates that environmental variables may be represented as probabilistic distributions of a priori information (priors). Priors for environmental variables that do not change much over time have been widely studied. Little is known, however, about how priors develop in environments with nonstationary statistics. We examine whether humans change their reliance on the prior based on recent changes in environmental variance. Through experimentation, we obtain an online estimate of the human sensorimotor prior (prediction) and then compare it to similar online predictions made by various nonadaptive and adaptive models. Simulations show that models that rapidly adapt to nonstationary components in the environments predict the stimuli better than models that do not take the changing statistics of the environment into consideration. We found that adaptive models best predict participants' responses in most cases. However, we find no support for the idea that this is a consequence of increased reliance on recent experience just after the occurrence of a systematic change in the environment.

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