Modeling Machine Health Using Gated Recurrent Units with Entity Embeddings and K-Means Clustering

We describe a machine learning approach for predicting machine health indicators two weeks into the future. The model developed uses a neural network architecture that incorporates sensor data inputs using gated recurrent units with metadata inputs using entity embeddings. Both inputs are then concatenated and fed to a fully connected neural network classifier. Furthermore, our classes are generated by clustering the continuous sensor values of the training data using K-Means. To validate the model we performed an ablation study in order to verify the effectiveness of each of the model’s components, and also compared our approach to the typical method of predicting continuous scalar values.

[1]  Ruqiang Yan,et al.  Machine health monitoring with LSTM networks , 2016, 2016 10th International Conference on Sensing Technology (ICST).

[2]  Rik Van de Walle,et al.  Deep Learning for Infrared Thermal Image Based Machine Health Monitoring , 2017, IEEE/ASME Transactions on Mechatronics.

[3]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[4]  Chetan Gupta,et al.  Long Short-Term Memory Network for Remaining Useful Life estimation , 2017, 2017 IEEE International Conference on Prognostics and Health Management (ICPHM).

[5]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[6]  Li Lin,et al.  Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network , 2016, 2016 IEEE International Conference on Aircraft Utility Systems (AUS).

[7]  Lovekesh Vig,et al.  Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder , 2016, ArXiv.

[8]  Huijun Gao,et al.  Data-Based Techniques Focused on Modern Industry: An Overview , 2015, IEEE Transactions on Industrial Electronics.

[9]  Cheng Guo,et al.  Entity Embeddings of Categorical Variables , 2016, ArXiv.

[10]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[11]  Chi-Man Vong,et al.  Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning , 2017, IEEE Transactions on Industrial Informatics.

[12]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[13]  Ruqiang Yan,et al.  Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks , 2017, Sensors.

[14]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[15]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[16]  Fei Shen,et al.  Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks , 2018, IEEE Transactions on Industrial Electronics.

[17]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[18]  Melissa Aczon,et al.  Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks , 2017, ArXiv.

[19]  Haibo He,et al.  Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis , 2017, IEEE Transactions on Instrumentation and Measurement.

[20]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[21]  Pascal Vincent,et al.  Artificial Neural Networks Applied to Taxi Destination Prediction , 2015, DC@PKDD/ECML.

[22]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[23]  Miao He,et al.  Deep Learning Based Approach for Bearing Fault Diagnosis , 2017, IEEE Transactions on Industry Applications.

[24]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[25]  Yang Chang,et al.  Weighted Data-Driven Fault Detection and Isolation: A Subspace-Based Approach and Algorithms , 2016, IEEE Transactions on Industrial Electronics.