Fault Detection based on Information Extraction from Measured Time-series Data in Building Air-conditioning System

2015 ◽  
Vol 135 (6) ◽  
pp. 651-659 ◽  
Author(s):  
Masaki Yumoto
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Information Extraction ◽  
Air Conditioning ◽  
Time Series Data ◽  
Series Data ◽  
Air Conditioning System ◽  
Measured Time

scholarly journals Multiscale Sample Entropy of Two-Dimensional Decaying Turbulence

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 245
Author(s):  
Ildoo Kim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Soap Film ◽  
Sample Entropy ◽  
Series Data ◽  
Entropy Analysis ◽  
Measured Time ◽  
Energetic Analysis ◽  
Turbulence Data ◽  
Decaying Turbulence

Multiscale sample entropy analysis has been developed to quantify the complexity and the predictability of a time series, originally developed for physiological time series. In this study, the analysis was applied to the turbulence data. We measured time series data for the velocity fluctuation, in either the longitudinal or transverse direction, of turbulent soap film flows at various locations. The research was to assess the feasibility of using the entropy analysis to qualitatively characterize turbulence, without using any conventional energetic analysis of turbulence. The study showed that the application of the entropy analysis to the turbulence data is promising. From the analysis, we successfully captured two important features of the turbulent soap films. It is indicated that the turbulence is anisotropic from the directional disparity. In addition, we observed that the most unpredictable time scale increases with the downstream distance, which is an indication of the decaying turbulence.

Weakly Supervised Deep Neural Network for Bearing Fault Diagnosis

2020 ◽  
Author(s):  
Daisuke Miki ◽  
Kazuyuki Demachi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Fault Detection ◽  
High Performance ◽  
Time Series Data ◽  
Vibration Signal ◽  
Detection Methods ◽  
Series Data ◽  
Vibration Signals ◽  
Automatic Feature Extraction

Abstract Bearings are one of the main components of rotating machinery, and their failure is one of the most common cause of mechanical failure. Therefore, many fault detection methods based on artificial intelligence, such as machine learning and deep learning, have been proposed. Particularly, with recent advances in deep learning, many anomaly detection methods based on deep neural networks (DNN) have been proposed. DNNs provide high-performance recognition and are easy to implement; however, optimizing DNNs require large annotated datasets. Additionally, the annotation of time-series data, such as abnormal vibration signals, is time consuming. To solve these problems, we proposed a method to automatically extract features from abnormal vibration signals from the time-series data. In this research, we propose a new DNN training method and fault detection method inspired by multi-instance learning. Additionally, we propose a new loss function for optimizing the DNN model that identifies anomalies from a time-series data. Furthermore, to evaluate the feasibility of automatic feature extraction from vibration signal data using the proposed method, we conducted experiments to determine whether anomalies could be detected, identified, and localized in published datasets.

scholarly journals Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jiusheng Chen ◽  
Xingkai Xu ◽  
Xiaoyu Zhang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Fault Detection ◽  
Data Stream ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Turbine Engine ◽  
Detection Model ◽  
Redundant Data

Fault detection for turbine engine components is becoming increasingly important for the efficient running of commercial aircraft. Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. In this paper, an adaptive weighted one-class SVM-based fault detection method coupled with incremental and decremental strategy is proposed, which can efficiently solve the time series data stream drifting problem. To update the efficient training of the fault detection model, the incremental strategy based on the new incoming data and support vectors is proposed. The weight of the training sample is updated by the variations of the decision boundaries. Meanwhile, to increase the calculating speed of the fault detection model and reduce the redundant data, the decremental strategy based on the k-nearest neighbor (KNN) is adopted. Based on time series data stream, numerical simulations are conducted and the results validated the superiority of the proposed approach in terms of both the detection performance and robustness.

scholarly journals Model Parameter Based Fault Detection for Time-series Data

2011 ◽  
Vol 20 (4) ◽  
pp. 67-79 ◽  
Author(s):  
Si-Jeo Park ◽  
Cheong-Sool Park ◽  
Sung-Shick Kim ◽  
Jun-Geol Baek
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Time Series Data ◽  
Series Data ◽  
Model Parameter

Stroscope: Multi-Scale Visualization of Irregularly Measured Time-Series Data

2014 ◽  
Vol 20 (5) ◽  
pp. 808-821 ◽  
Author(s):  
Myoungsu Cho ◽  
Bohyoung Kim ◽  
Hee-Joon Bae ◽  
Jinwook Seo
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Multi Scale ◽  
Measured Time

scholarly journals RAIM-NET: A Deep Neural Network for Receiver Autonomous Integrity Monitoring

2020 ◽  
Vol 12 (9) ◽  
pp. 1503
Author(s):  
Yuan Sun
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Detection ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Main Idea ◽  
Series Data ◽  
Model Parameters ◽  
Integrity Monitoring ◽  
Receiver Autonomous Integrity Monitoring

With the continuous popularization of Global Navigation Satellite System (GNSS) in various applications, the performance requirement for integrity is also increasing, especially in the field of safety-of-life. Although the existing Receiver Autonomous Integrity Monitoring (RAIM) algorithm has been embedded in the GNSS receiver as a standard method, it might still suffer from small fault detection and delay alarm problem for time series fault models. In an effort to solve this problem, a Deep Neural Network (DNN) for RAIM, named RAIM-NET, is investigated in this paper. The main idea of RAIM-NET is to propose a combination of feature vector extraction and DNN model to improve the performance of integrity monitoring, with a problem specifically designed for loss function, obtaining the model parameters. Inspired by the powerful advantages of Recurrent Neural Network (RNN) in time series data processing, a multilayer RNN is applied to build the DNN model structure and improve the detection rate for small faults and reduce the alarm delay for the time series fault event. Finally, real GNSS data experiments are designed to verify the performance of RAIM-NET in fault detection and time delay for integrity monitoring.

scholarly journals On Practical Aspects of Using RNNs for Fault Detection in Sparsely-labeled Multi-sensor Time Series

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Narendhar Gugulothu ◽  
Vishnu TV ◽  
Priyanka Gupta ◽  
Pankaj Malhotra ◽  
Lovekesh Vig ◽  
...  
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Time Series Data ◽  
Unlabeled Data ◽  
Training Data ◽  
Series Data ◽  
Small Subset ◽  
Multiple Sensors ◽  
Unsupervised Approach ◽  
Hidden Layer

In this work, we attempt to address two practical limitations when using Recurrent Neural Networks (RNNs) as classifiers for fault detection using multi-sensor time series data: Firstly, there is a need to understand the classification decisions of RNNs. It is difficult for engineers to diagnose the faults when multiple sensors are being monitored at once. The faults detected by RNNs can be better understood if the sensors carrying the faulty signature are known. To achieve this, we propose a sensor relevance scoring (SRS) approach that scores each sensor based on its contribution to the classification decision by leveraging the hidden layer activations of RNNs. Secondly, lack of labeled training data due to infrequent faults (or otherwise) makes it difficult to train RNNs in a supervised manner. We pre-train an RNN on large unlabeled data via an autoencoder in an unsupervised manner, and then finetune the RNN for the fault detection task using small amount of labeled training data. Through experiments on a public gasoil heating loop dataset and a proprietary pump dataset, we demonstrate the efficacy of the proposed solutions, and show that i) SRS can help point to the sensors relevant for a fault, ii) large unlabeled data can be used to pre-train an RNNbased fault detector in an unsupervised manner in sparselylabeled scenarios, and iii) a purely unsupervised approach for fault detection (e.g. based on RNN-autoencoders) may not suffice when the number of sensors being monitored is large while the signature for fault is present in only a small subset of sensors.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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