Diverse experience and talent

Our team has a range of professional experience and education that brings insight and value to our work

Decades
of relevant industry experience
Published Research
in machine learning and predictive maintenance

Automating Predictive Maintenance Using State-Based Transfer Learning and Ensemble Methods

Published at 2021 IEEE International Symposium on Robotic and Sensors Environments

This paper aims to provide a methodology for combining tools and presents a framework for an automated machine learning (AutoML) process by using a combination of pre-existing AutoML tools and new transfer learning methodologies. The framework uses recent observations in vibration analysis and deep learning architectures to abstract away many of the small, but intricate, decisions required for data preparation and model building. This allows for a unique data science strategy and dramatic reduction in the development time required to achieve competitive baseline models when compared to industry averages, while simultaneously improving the ability to address unseen environments.

A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series

While LSTMs show increasingly promising results for forecasting Financial Time Series (FTS), this paper seeks to assess if attention mechanisms can further improve performance. The hypothesis is that attention can help prevent long-term dependencies experienced by LSTM models. To test this hypothesis, the main contribution of this paper is the implementation of an LSTM with attention. Both the benchmark LSTM and the LSTM with attention were compared and both achieved reasonable performances of up to 60% on five stocks from Kaggle's Two Sigma dataset. This comparative analysis demonstrates that an LSTM with attention can indeed outperform standalone LSTMs but further investigation is required as issues do arise with such model architectures...

Towards Generalization of Intelligent Fault Detection for Roller Element Bearings via Distinct Dataset Transfer Learning

Published at ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

Predictive maintenance provides for the possibility of service schedule, inventory, maintenance, and safety optimization. However, real-world rotating machinery undergo a variety of operating conditions, fault conditions, and noise. Due to these factors, it is often required that a fault detection algorithm perform accurately even on data outside its trained domain. Although open-source datasets offer an incredible opportunity to advance the performance of predictive maintenance technology and methods, more research is required to develop algorithms capable of generalized intelligent fault detection across domains and discrepancies....

A New Vibration-Based Algorithm for Operational Machine State Identification

27th international Congress on Sound and Vibration

Large strides in the field of predictive maintenance have been taken towards reducing downtime and optimizing operating schedules. However, extracting actionable insight from vibration data coming from rotating machinery working with variable external loads remains challenging. Due to the realities of machinery at work in real-world environments, many factors can contribute to abnormal and misleading signals: quality of the connection, infiltration of noise, and the natural task (often varying) of the machine at hand...

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