Bayesian Quickest Change Point Detection With Multiple Candidates of Post-Change Models

Nath, Samrat; Wu, Jingxian

doi:10.1109/globalsip.2018.8646596

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Generally, change detection methods can be classified into two categories, Bayesian and non-Bayesian (minimax) methods. If the prior probability of the change point is known, then the methods are Bayesian procedures, such as [30], [31]. On the other hand, when the prior probability of the change point is unknown, the low latency change detection methods are developed under the minimax or non-Bayesian criterion.…”

Section: In Addition Online Diagnosis Methods Allow Automatic Remotementioning

confidence: 99%

Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Nath

Zhao

et al. 2020

IEEE Access

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Low latency change detection aims to minimize the detection delay of an abrupt change in probability distributions of a random process, subject to certain performance constraints such as the probability of false alarm (PFA). In this paper, we study the low latency detection of bearing faults of direct-drive wind turbines (WT), by analyzing the statistical behaviors of stator currents generated by the WT in real-time. It is discovered that the presence of fault will affect the statistical distribution of WT stator current amplitude at certain frequencies. Since the signature of a fault can appear in one of the multiple possible frequencies, we need to monitor the signals on multiple frequencies simultaneously, and each possible frequency is denoted as a candidate. Based on the unique properties of WT bearing faults, we propose a new multi-candidate low latency detection algorithm that can combine the statistics of signals from multiple candidate frequencies. The new algorithm does not require a separate training phase, and it can be directly applied to the stator current data and perform online detection of various possible bearing faults. The theoretical performance of the proposed algorithm is analytically identified in the form of upper bounds of the PFA and average detection delay (ADD). The algorithm allows flexible parametric adjustment of the tradeoff between PFA and ADD. INDEX TERMS Bearing fault, fault detection, quickest change detection, wind turbine.

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Section: In Addition Online Diagnosis Methods Allow Automatic Remotementioning

confidence: 99%

Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Nath

Zhao

et al. 2020

IEEE Access

Self Cite

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“…Offline approaches handle the data set as a batch and identify the locations of the points by looking back to the whole data set. Many of the Bayesian studies work offline on a fixed size data [25][26][27]. On the other hand, online approaches work on a sequential data and aim to detect the locations of the CPs as soon as possible after they occur.…”

Section: Related Workmentioning

confidence: 99%

Change Point Detection Methods for Locating Activations in Functional Neuronal Images

Candemir

Oğuz

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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The most common analysis for fMRI images is activation detection, in which the purpose is to find the locations in the brain that respond to specific functions, such as visual processing or motor functions by providing related stimuli as tasks in the experiment. On the other hand, it is also important to detect the instance the activation is triggered. One of the powerful techniques that can analyze the abnormal behavior of any data is change point (CP) analysis. We suggest that CP detection algorithms also can be used to locate the activations in functional magnetic resonance imaging (fMRI) sequences, as well. Our paper presents a two-fold innovative study in that respect. First, we propose to use CP detection algorithms to locate the activations in fMRI signals as a state-of-art topic. Furthermore, we propose and compare a set of change point analysis methods, a regression-based method (RBM), a statistical method (SM), and a mean difference of double sliding windows method (MDSW)) to locate such points. Second, we apply these methods to the fMRI signals, which are acquired from the real subjects, while they were performing fMRI tasks. Proposed methods were applied to three different fMRI experiments with a motor task, a visual task, and a linguistic task. The analysis shows that the methods find activations in accordance with established methods such as statistical parametric maps (SPM). The acquired up to 94 % results also show that the proposed methods can be used effectively to locate the activation times on fMRI time series.

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Quickest change point detection with multiple postchange models

Nath

2020

Sequential Analysis

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Bayesian Quickest Change Point Detection With Multiple Candidates of Post-Change Models

Cited by 5 publications

References 16 publications

Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Change Point Detection Methods for Locating Activations in Functional Neuronal Images

Quickest change point detection with multiple postchange models

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