Remaining useful life estimation using long short-term memory deep learning

Hsu, Che-Sheng; Jiang, Jehn-Ruey

doi:10.1109/icasi.2018.8394326

Cited by 45 publications

(32 citation statements)

References 10 publications

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“…Another option is to obtain the desired output value based on the appropriate degradation model. Referring to the current literature, this paper adopts piece-wise linear degradation model to determine the target RUL [ 37 , 38 , 39 , 40 ]. Piece-wise linear regression model can prevent the algorithm from overestimating RUL.…”

Section: Experiments and Analysismentioning

confidence: 99%

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

Cheng

Chen

et al. 2021

Sensors

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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.

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Section: Experiments and Analysismentioning

confidence: 99%

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

Cheng

Chen

et al. 2021

Sensors

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“…The characteristics of the LSTM make it a natural choice for machinery RUL estimation due to the considerable time lag between inputs and their corresponding outputs. A simple LSTM is employed in [65] and a bidirectional LSTM is proposed in [66] for RUL estimation. A hybrid deep learning model combining CNN and LSTM is demonstrated for machine health monitoring in [67], where a CNN is employed for local features extraction and bi-directional LSTM [68] is demonstrated and built on CNN outputs for the temporal information encoding and representation learning.…”

Section: B Machine Learning Based Approachesmentioning

confidence: 99%

Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion

et al. 2020

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Estimation of Remaining Useful Life (RUL) is a crucial task in Prognostics and Health Management (PHM) for condition-based maintenance of machinery. In order to transmit and store the sensor data for archiving and long term analysis, data compression techniques are regularly used to reduce the requirements of bandwidth, energy and storage in modern remote PHM systems. In these systems the challenge arises of how the compressed sensor data affects the RUL estimation algorithms. A main drawback of conventional statistical modeling approaches is that they require expert prior knowledge and a significant number of assumptions. Alternative regression based approaches and deep neural networks are known to have issues when modeling long-term dependencies in the sequential data. Recently Long Short-Term Memory (LSTM) neural networks have been proposed to overcome these issues and in this paper we create a LSTM network and data fusion approach that can estimate the RUL with compressed (distorted) data. The experimental results indicate that the proposed method is able to estimate RUL reliably with narrower error bands compared to other state-of-the-art approaches. Moreover, the proposed method is able to predict RUL from both the raw and compressed datasets with comparable accuracy. INDEX TERMS Machine health monitoring, remaining useful life (RUL), long-short term memory, recurrent neural network, data compression.

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“…adalah turunan dari Recurrent Neural Network (RNN) yang terbukti berhasil digunakan untuk prediksi data time series [3]. RNN…”

Section: Long Short Term Memory (Lstm)unclassified

Prediksi Data Transaksi Penjualan Time Series Menggunakan Regresi LSTM

Ashari¹,

Sadikin

2020

j. nas. pendidik. teknik. inform.

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Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

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Remaining useful life estimation using long short-term memory deep learning

Cited by 45 publications

References 10 publications

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion

Prediksi Data Transaksi Penjualan Time Series Menggunakan Regresi LSTM

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