(63a) Low-Touch Machine Learning Is Fulfilling the Promise of APM

Authors: 
Castillo, P., Aspentech
Williams, C., Aspentech
New methods and cutting-edge technologies are driving asset performance management (APM) well beyond historical capabilities, rapidly increasing its bottom-line value. Technologies such as cloud computing, data science and machine learning are now being integrated with automated methodologies directly into APM solutions.

This wave of integration firmly places advanced analytical techniques into the hands of operators and engineers with previously unimagined scale and ease of use. The incremental progress in APM over the last 20 years pales in comparison to what’s now possible through digital transformation.

Low-touch machine learning is the key catalyst to scale APM’s potential well beyond existing first principles-based solutions and “armies” of consultant engineers and data scientists. A widespread integration of machine learning in APM will mark a transition from estimated engineering and statistical models towards measuring actual patterns of asset behavior.

Manufacturing facilities staff can now readily extract value from decades of existing design and operations data to better manage and optimize asset performance. This “low-touch” machine learning method continuously embraces changes in asset behavior, empowering real-time APM value creation. Vetted and tested across diverse industries, scalable across multiple assets and powered by cloud and parallel computing, low-touch machine learning ushers in a new era of performance and optimization for all industries.