Supervised Operational Change Point Detection using Ensemble Long-Short Term Memory in a Multicomponent Industrial System

Gupta, Ashit; Masampally, Vishnu Swaroopji; Jadhav, Vishal; Deodhar, Anirudh; Runkana, Venkataramana

doi:10.1109/sami50585.2021.9378683

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…This is an interesting observation since the inclusion of additional sensors is typically expected to improve or at least maintain the prediction accuracy of deep learning models. However, Gupta et al (2021) have also reported a similar observation that incorporating sensors from all the components did not improve the predictions of TOAB from one component. Therefore, only IC models, i.e., CC-LSTM models with only 10 sensors corresponding to each component were considered for evaluating the ECC-LSTM approach.…”

Section: Dependency Among Componentsmentioning

confidence: 73%

Ensemble Deep Learning for Detecting Onset of Abnormal Operation in Industrial Multi-component Systems

Selvanathan

Nistala

Runkana

et al. 2022

IJPHM

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Breakdowns and unplanned shutdowns in industrial processes and equipment can lead to significant loss of availability and revenue. It is imperative to perform optimal maintenance of such systems when signs of abnormal behavior are detected and before they propagate and lead to catastrophic failure. This is particularly challenging in systems with interconnected multiple components as it is difficult to isolate the effect of one component on the operation of other components in the system. In this work, an ensemble approach based on Cascaded Convolutional neural network and Long Short-term Memory (CC-LSTM) network models is proposed for detecting and predicting the time of onset of faults in interconnected multicomponent systems. The performance of the ensemble CC-LSTM model was demonstrated on an industrial 4-component system and was found to improve the accuracy of onset time predictions by ~15% compared to individual CC-LSTM models and ~25-40% compared to commonly used deep learning techniques such as dense neural networks, convolutional neural networks and LSTMs. The CC-LSTM and the ensemble models also had the lowest missed detection rates and zero false positive rates making them ideal for real-time monitoring and fault detection in multicomponent systems.

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Section: Dependency Among Componentsmentioning

confidence: 73%

Ensemble Deep Learning for Detecting Onset of Abnormal Operation in Industrial Multi-component Systems

Selvanathan

Nistala

Runkana

et al. 2022

IJPHM

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show abstract

“…Change point detection is crucial in detecting the early signs of deterioration to prevent industrial equipment from unexpected disruptions [54].…”

Section: Change Point Detectionmentioning

confidence: 99%

Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering

Ensarioğlu,

İnkaya,

Emel

2023

Applied Sciences

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Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional–convolutional neural network–long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics.

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“…Among the NN models, RNN models have also demonstrated potential for change point detection. For example, an RNN model was proposed to predict the change points from data of sensors attached to industrial equipment parts [13]. Moreover, an RNN model outperformed the previous method in detecting the change points in multiple time-series load data of an electric power company for power outage analysis [14].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network to Predict Saccade Offset Time Points from Electrooculogram Signals for Automatic Measurement of Eye-Fixation-Related Potential

2023

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Eye-fixation-related potential (EFRP)—an event-related potential that is time-locked to the saccade offset (SO)—can be measured without synchronizing with time when external stimuli occur. Such an advantage in measurement enables the mean amplitude of the EFRP to be used to estimate the cognitive workload, which is known to change the amplitude, under real-world conditions. However, to observe EFRPs reliably, the SO timing must be correctly and consistently determined in milliseconds owing to the high temporal resolution of the electroencephalogram (EEG). As the electrooculogram (EOG) is commonly measured simultaneously with the EEG and the SO timing is reflected as a steep change in the waveforms, attempts have been made to determine the SO timing from EOG signals visually (the VD method). However, the SO timing detected by the VD method may be inconsistent across trials. We propose a gated recurrent unit—a recurrent neural network model—to detect the SO timing from EOGs consistently and automatically. We used EOG data from a task that mimics visual inspections, in which participants periodically traversed their eyes from left to right, for the model training. As a result, the amplitudes of the EFRPs based on the proposed method were significantly larger than those based on the VD method and the previous automatic method. This suggests that the proposed method can prevent the decrease in EFRP amplitudes owing to the inconsistent determination of the SO timing and increase the applicability of cognitive workload estimation using the EFRP in real-world environments.

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Supervised Operational Change Point Detection using Ensemble Long-Short Term Memory in a Multicomponent Industrial System

Cited by 4 publications

References 10 publications

Ensemble Deep Learning for Detecting Onset of Abnormal Operation in Industrial Multi-component Systems

Ensemble Deep Learning for Detecting Onset of Abnormal Operation in Industrial Multi-component Systems

Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering

Recurrent Neural Network to Predict Saccade Offset Time Points from Electrooculogram Signals for Automatic Measurement of Eye-Fixation-Related Potential

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