Mr. Maksim Bobrov Profile

Maksim Bobrov

at Lockheed Martin Corp

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Proceedings Article | 14 June 2023 Presentation + Paper

SAD: self-supervised avionic diagnostics

Attiano Purpura-Pontoniere, Maksim Bobrov, Tarun Bhattacharya

Proceedings Volume 12522, 125220F (2023) https://doi.org/10.1117/12.2664732

KEYWORDS: Data modeling, Principal component analysis, Binary data, Machine learning, Diagnostics, Image restoration, Visualization, Education and training, Image processing, Transformers

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Oftentimes aircrafts will experience flight failures that produce a significant amount of complicated avionic diagnostic data, wherein many reports of faults will contain false positives across various subsystems, and true faults normally are identified by pouring through an entire flight's diagnostic logs. We investigated if avionic fault detection can be improved, or completely automated, through intelligent application of machine learning models to paired instrumentation and natural language time-series data from helicopters. We focused on using an unsupervised model, specifically an auto-encoder, as our data was unlabeled. We also focused on the natural language portion of the data, and we created a novel transformation from natural language time series data to image data for ease of model integration, taking inspiration from the spectrogram. This allowed us to leverage a linear and convolutional autoencoder for feature extraction, which we compared to a deterministic algorithm like Principal Component Analysis (PCA). We successfully trained convolutional autoencoders to reconstruct our avionic diagnostic images. We attempted to train linear autoencoders with our custom images and a bit transformation of our avionic diagnostic images. The linear model was unable to reconstruct the image and binary version. Finally we explored if the embeddings of the convolutional autoencoder and PCA could be used to automatically label our data by exploring the clusters produced by K-means. Our results were promising, where the PCA was a more accurate fault detector than the convolutional autoencoder, but further investigation is needed.

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