Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

Gugulothu, Narendhar; Tv, Vishnu; Malhotra, Pankaj; Vig, Lovekesh; Agarwal, Puneet; Shroff, Gautam

doi:10.36001/ijphm.2018.v9i1.2689

Cited by 49 publications

(59 citation statements)

References 36 publications

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“…However, the neural networks need high computation power and, for supervised learning, a pre-labeled data set is required. In future work, recurrent neural networks [3] may provide further improvements, as they include previous decisions into a classification. Other approaches dealing with the application of neural networks for sensor data exist.…”

Section: Discussionmentioning

confidence: 99%

CONSENSORS: A Neural Network Framework for Sensor Data Analysis

Hoppenstedt

Pryss

Kammerer

et al. 2019

On the Move to Meaningful Internet Systems: OTM 2018 Workshops

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Machine breakdowns in industrial plants cause production delays and financial damage. In the era of cyber-physical systems, machines are equipped with a variety of sensors to monitor their status. For example, changes to sensor values might indicate an abnormal behavior and, in some cases, detected anomalies can be even used to predict machine breakdowns. This procedure is called predictive maintenance, which pursues the goal to increase machine productivity by reducing down times. Thereby, anomalies can be either detected by training data models based on historic data or by implementing a self-learning approach. In this work, the use of neural networks for detecting anomalies is evaluated. In the considered scenarios, anomaly detection is based on temperature data from a press of a machine manufacturer. Based on this, a framework was developed for different types of neural networks as well as a high-order linear regression approach. We use the proposed neural networks for restoring missing sensor values and to improve overall anomaly detection. An evaluation of the used techniques revealed that the high-order linear regression and an autoencoder constitute best practices for data recovery. Moreover, deep neural networks, especially convolutional neural networks, provide the best results with respect to overall anomaly detection.

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Section: Discussionmentioning

confidence: 99%

CONSENSORS: A Neural Network Framework for Sensor Data Analysis

Hoppenstedt

Pryss

Kammerer

et al. 2019

On the Move to Meaningful Internet Systems: OTM 2018 Workshops

View full text Add to dashboard Cite

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“…Bayesian model [235,236] DNN [162] RNN [237] Regression-based model [137] prediction. The advantages of this method are more prominent and can be extended to online RUL evaluation.…”

Section: Dtmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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As a transmission component, the gear has been obtained widespread attention. The remaining useful life (RUL) prediction of gear is critical to the prognostics health management (PHM) of gear transmission systems. The digital twin (DT) provides support for gear RUL prediction with the advantages of rich health information data and accurate health indicators (HI). This paper reviews digital twin-driven RUL prediction methods for gear performance degradation, from the view of digital twin-driven physical model-based and virtual model-based prediction method. From the view of physical model-based one, it includes prediction model based on gear crack, gear fatigue, gear surface scratch, gear tooth breakage, and gear permanent deformation. From the view of digital twin-driven virtual model-based one, it includes non-deep learning methods, and deep learning methods. Non-deep learning methods include wiener process, gamma process, hidden Markov model (HMM), regression-based model, proportional hazard model. Deep learning methods include deep neural networks (DNN), deep belief networks (DBN), convolutional neural networks (CNN), and recurrent neural networks (RNN), etc. It mainly summarizes the performance degradation and life test of various models in gear, and evaluates the advantages and disadvantages of various methods. In addition, it encourages future works.

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“…30,32,33 Convolution neural network (CNN). Convolutional neural network (CNN) is a class of deep neural networks, most commonly applied to analyzing visual 26 Lu et al, 27 Jia et al, 21 Mao et al, 28 45 Malhotra et al, 46 Gugulothu and Gugulothu, 47 Zheng et al 48…”

Section: Algorithmsmentioning

confidence: 99%

“…Particularly, the LSTM network without longterm time dependency problems by controlling information flow using input gate, forget gate, and output gate, has been successfully used for many sequence learning and temporal modeling tasks, such as machine translation 54 and speech recognition. 55 In the field of predictive maintenance, due to inherent sequential nature of sensor data, LSTMs are well-suited for system anomaly detection 49,50,56 and RUL prediction, [45][46][47][48] which can make full use of the sensor sequence information and expose hidden patterns within sensor data under various operating conditions. Malhotra et al 49 proposed LSTM based encoder-decoder scheme for anomaly detection, which learns to reconstruct multivariate time-series in a normal state and then uses reconstruction error to detect anomalies.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Applications of deep learning in big data analytics for aircraft complex system anomaly detection

Ning

Sun

Liu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Big data analytics with deep learning approach have attracted increasing attention in transportation engineering, involving operations, maintenance, and safety. In commercial aviation sectors, operational, and maintenance data produced on modern aircraft is increasing exponentially, and predictive analysis of these data is an exciting and promising field in aviation maintenance, which has a potential to revolutionize aerospace maintenance industry. This study illustrates the state-of-the-art applications of deep learning in big data analytics for predictive maintenance and a real-world case study for commercial aircraft. A Long Short-Term Memory network based Auto-Encoders (LSTM-AE) is proposed for complex aircraft system fault detection and classification, which makes use of the raw time-series data from heterogeneous sensors. The proposed method uses nominal time-series samples corresponding to healthy behavior of the system to learn a reconstruction model based on LSTM-AE framework. Then the system health index (HI) and fault feature vectors are derived from the reconstruction error matrix for fault detection and classification. The proposed method is demonstrated on a real-world data set from a commercial aircraft fleet. The typical PCV faults as well as the 390 F sensor and 450 F sensor faults due to sense line air leakage are successfully detected and distinguished based on the extracted features. The case study results show that the computed HI can effectively characterize the health state of the aircraft system and different fault types can be identified with high confidence, which is helpful for line fault troubleshooting.

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Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

Cited by 49 publications

References 36 publications

CONSENSORS: A Neural Network Framework for Sensor Data Analysis

CONSENSORS: A Neural Network Framework for Sensor Data Analysis

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Applications of deep learning in big data analytics for aircraft complex system anomaly detection

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