Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone

Zhou, Yexu; Hefenbrock, Michael; Huang, Yiran; Riedel, Till; Beigl, Michael

doi:10.1007/978-3-030-67667-4_28

Cited by 12 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sliding window approach can give more context and improve feature extraction ( Zhou et al, 2021 ). In this scenario, all of the variables in the window are connected to an LSTM layer.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

Kamat

Sugandhi

Kumar

2021

PeerJ Computer Science

View full text Add to dashboard Cite

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

show abstract

“…The sliding window approach can give more context and improve feature extraction ( Zhou et al, 2021 ). In this scenario, all of the variables in the window are connected to an LSTM layer.…”

Section: Methodsmentioning

confidence: 99%

“…Because the fully connected layer of the LSTM (FCLSTM) requires one-dimensional input for each time step, spatial and feature structure must be anticipated. Furthermore, the model complexity (number of weights) rises linearly with window size, which may be addressed with convolutional LSTMs in some cases ( Zhou et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

Kamat

Sugandhi

Kumar

2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Deep learning has shown admirable performance in the prognosis of a high amount of data [ 16 ]. Among the popular deep prognostic models are Convolutional Neural Network (CNN) [ 5 , 6 , 7 , 11 ] and Recurrent neural network—particularly its long short-term memory [ 4 , 17 , 18 , 19 , 20 ] version.…”

Section: Introductionmentioning

confidence: 99%

On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse

Mihigo

Zennaro

Uwitonze

et al. 2022

Sensors

View full text Add to dashboard Cite

A precise prediction of the health status of industrial equipment is of significant importance to determine its reliability and lifespan. This prediction provides users information that is useful in determining when to service, repair, or replace the unhealthy equipment’s components. In the last decades, many works have been conducted on data-driven prognostic models to estimate the asset’s remaining useful life. These models require updates on the novel happenings from regular diagnostics, otherwise, failure may happen before the estimated time due to different facts that may oblige rapid maintenance actions, including unexpected replacement. Adding to offline prognostic models, the continuous monitoring and prediction of remaining useful life can prevent failures, increase the useful lifespan through on-time maintenance actions, and reduce the unnecessary preventive maintenance and associated costs. This paper presents the ability of the two real-time tiny predictive analytics models: tiny long short-term memory (TinyLSTM) and sequential dense neural network (DNN). The model (TinyModel) from Edge Impulse is used to predict the remaining useful life of the equipment by considering the status of its different components. The equipment degradation insights were assessed through the real-time data gathered from operating equipment. To label our dataset, fuzzy logic based on the maintainer’s expertise is used to generate maintenance priorities, which are later used to compute the actual remaining useful life. The predictive analytic models were developed and performed well, with an evaluation loss of 0.01 and 0.11, respectively, for the LSTM and model from Edge Impulse. Both models were converted into TinyModels for on-device deployment. Unseen data were used to simulate the deployment of both TinyModels. Conferring to the evaluation and deployment results, both TinyLSTM and TinyModel from Edge Impulse are powerful in real-time predictive maintenance, but the model from Edge Impulse is much easier in terms of development, conversion to Tiny version, and deployment.

show abstract

Towards Generalizable Machinery Prognostics

Baǧdelen

Paulheim

Döhring

et al. 2022

Advanced Data Mining and Applications

View full text Add to dashboard Cite

Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone

Cited by 12 publications

References 18 publications

Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse

Towards Generalizable Machinery Prognostics

Contact Info

Product

Resources

About