Mtelligence Announces SaaS Equipment Condition Machine Learning Platform for Manufacturers
17 January 2011
Mtelligence has released the first machine learning platform with turnkey integration to all leading operations and maintenance systems used in manufacturing
Manufacturers spend a large portion of their budget on maintenance and plant automation systems, and yet are still plagued by equipment failure, wasteful energy usage and reduced product quality. mVision applies a combination of supervised and unsupervised learning techniques to determine predictors of equipment failure and then continuously monitors for them.Alan Johnston, President of the MIMOSA Foundation, commented: "I am genuinely excited to see the team continuing to add more capabilities to their offerings and to make those capabilities available via a SaaS model leveraging the MIMOSA standards. Innovative startups like Mtelligence will be an important part of helping lead the way to gaining more value from new generations of technology and new paradigms such as machine learning. As long-term members of MIMOSA, Mtelligence has realized the value proposition of leveraging MIMOSA standards to enable them to focus more of their resources on adding value to their clients."
"Machine learning hasn't hit mainstream in manufacturing, in part due to the effort required to build an accurate model and get the data needed to train the system," said Alex Bates, Mtelligence chief technology officer. "Meanwhile, plant engineers and maintenance managers have been struggling to make sense of the mountains of data coming from various software packages. mVision enables customers to convert this data into profit."
The use of an open standard data model and messaging protocol enables mVision to integrate with a wide variety of data sources, and also enables extensibility. For example, production line health indicators from mVision can be integrated to MES (manufacturing execution system) and ERP packages for optimal scheduling decisions based on capability forecast.
"In continuous manufacturing environments, one of the toughest decisions is when to stop production for preventive maintenance," Bates said. "Preventive maintenance is often deferred, leading to increased component failures and reactive maintenance. With mVision, maintenance is prioritized based on the actual condition of the equipment."