Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery

Jayasinghe, Lahiru; Samarasinghe, Tharaka; Yuenv, Chau; Low, Jenny; Ge, Shuzhi Sam

doi:10.1109/icit.2019.8754956

Cited by 56 publications

(32 citation statements)

References 20 publications

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“…The more successful models [22,24] were not as shallow, thus suffers from high computational load which meant longer training times and slower deployment. Reference [25], like us, proposed temporal convolutions, however on top of recurrent LSTM cells. Other neural network methods may include using a sparse auto-encoder with logistic regression to predict the RUL [26] while [27] used a Directed Acyclic Graph (DAG) containing parallel LSTM and CNN architectures to solve their prediction problem.…”

Section: Related Workmentioning

confidence: 99%

Remaining Useful Life Prediction Using Temporal Convolution with Attention

Tan

Teo

2021

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Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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Section: Related Workmentioning

confidence: 99%

Remaining Useful Life Prediction Using Temporal Convolution with Attention

Tan

Teo

2021

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“…Similarly, 1D temporal convolutions are used to learn features relevant to time dependencies of sensor values. Then these extracted features are fed to a stacked LSTM network to learn the long short‐term time dependencies 33 …”

Section: Related Workmentioning

confidence: 99%

“…RNN/LSTM can extract the temporal feature related with time dependencies. Therefore, many methods combining CNN with LSTM are proposed because of the complementary strengths of CNN and LSTM 4,33 …”

Section: Introductionmentioning

confidence: 99%

Using temporal convolution network for remaining useful lifetime prediction

Chen

Liu

2020

Engineering Reports

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Because Convolutional Neural Network (CNN) can extract spatial feature, while Long Short‐Term Memory (LSTM) can learn temporal features, many methods combining CNN with LSTM are proposed for remaining useful lifetime prediction. In practice, it is better to learn temporal features from long history sequence data because of the slow inherently long‐term degradation process. However, LSTM is less efficient in processing long history sequence. To solve this problem, in this work, a temporal convolution network combining causal filters with dilated convolutions is used to expand the receptive field length of network. The network structure can be fixed through three key parameters, and the size of time window adopted for time sequence processing is the same as the receptive field length. These two characteristics allow the network to easily be applied for engineering purposes. The method is tested and evaluated using two well‐known datasets, namely the “Turbofan Engine Degradation Simulation Dataset C‐MAPSS” and “Milling Dataset.” The performance analysis shows that the proposed method outperforms more classical methods in terms of prediction accuracy.

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“…Recently, there has been much research conducted on feature learning using Convolutional Neural Networks [17]. With the recent development in CNN, it has been commonly applied in diverse real-world applications such as medical image analysis [18], time series analysis [19], speech recognition [20], etc.…”

Section: A Image Feature Learningmentioning

confidence: 99%

Feature Learning and Analysis for Cleanliness Classification in Restrooms

et al. 2019

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In order to revamp the cleaning contract from the head-count basis into a performance basis, a fair and unbiased cleanliness classification is necessary. However, the perception of cleanliness is very subjective to the observer. Hence, it is not an easy task to quantify the cleanliness. This paper presents an application of principal component analysis (PCA) in conjunction with convolutional neural networks (CNN) to identify the cleanliness of a restroom up to three levels; namely, dirty, average, and clean. The proposed method includes an application specific data augmentation algorithm and a PCA-based feature analysis schema to select the best suited CNN model for our dataset. Since this study focused on a specific application, we benchmark the performances of the proposed method performances with the state-of-the-art computer vision algorithms on our dataset. Moreover, our study shows a machine learning approach toward automating the inspection process of a restroom. INDEX TERMSImage classification, deep learning, principle component analysis, data augmentation, feature learning, internet of things.

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Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery

Cited by 56 publications

References 20 publications

Remaining Useful Life Prediction Using Temporal Convolution with Attention

Remaining Useful Life Prediction Using Temporal Convolution with Attention

Using temporal convolution network for remaining useful lifetime prediction

Feature Learning and Analysis for Cleanliness Classification in Restrooms

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