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Embeddings Based Parallel Stacked Autoencoder Approach for Dimensionality Reduction and Predictive Maintenance of Vehicles

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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2020, IoT Streams 2020)

Abstract

Predictive Maintenance (PdM) of automobiles requires the storage and analysis of large amounts of sensor data. This requirement can be challenging in deploying PdM algorithms onboard the vehicles due to limited storage and computational power on the hardware of the vehicle. Hence, this study seeks to obtain low dimensional descriptive features from high dimensional data using Representation Learning. The low dimensional representation can then be used for predicting vehicle faults, in particular a component related to powertrain. A Parallel Stacked Autoencoder based architecture is presented with the aim of producing better representations when compared to individual Autoencoders with focus on vehicle data. Also, Embeddings are employed on Categorical Variables to aid the performance of the Artificial Neural Networks (ANN) models. This architecture is shown to achieve excellent performance, and in close standards to the previous state-of-the-art research. Significant improvement in powertrain failure prediction is obtained along with reduction in size of input data using our novel deep learning ANN architecture.

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Correspondence to Reza Khoshkangini .

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Revanur, V., Ayibiowu, A., Rahat, M., Khoshkangini, R. (2020). Embeddings Based Parallel Stacked Autoencoder Approach for Dimensionality Reduction and Predictive Maintenance of Vehicles. In: Gama, J., et al. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. ITEM IoT Streams 2020 2020. Communications in Computer and Information Science, vol 1325. Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-66770-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66769-6

  • Online ISBN: 978-3-030-66770-2

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