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
Our team has a range of professional experience and education that brings insight and value to our work
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Repeat founder; previous venture raising $1.2M and scaling team to 20+
B. Eng + MASc in Machine Learning for Predictive Maintenance
25+ years of industry experience, previously VP Ops at DSL
Deep domain understanding of our customers’ pain points
25+ years of industry experience, previously Partner and CPA at Ernst & Young
Deep domain understanding of field service risk and finance
Repeat founder - previous venture raising $1.2M and scaling team to 20+
B. Eng in Electrical and Computer Science, top graduate in entire faculty (9.98GPA, Governors Medal)
30+ years of operations experience in the restaurant & hospitality industry, previously leading at Inspire Brands equipment, design & procurement
Deep understanding of foodservice chain operational pain points and restaurant P&L’s
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.
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...
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....
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...
We work with your internal stakeholders to learn about your company’s needs and gain a full understanding of your existing industrial environment. We believe in the value of measuring progress with meaningful metrics in order to guarantee your business will experience growth.
Our process always starts by defining your requirements and documentation a scope of the needs, probems, opportunities, and values to be addressed. Because your business needs and industrial environments are unique, so too will be your solution. We work to gain a full understanding of your existing industrial environment and employ iterative processes to integrate, extend, and amplify your existing assets.
Our Professional Services team coordinates directly with your key leaders and stakeholders. We employ our technology experts and partners for the delivery of the solution - from high level scoping to successful outcomes.
