SpaceTime Insight’s machine learning analytics, powered by our Lateo machine learning library, predict the probable time of asset failure without loss of confidence. You can “see over the hill” and understand your risk of actually surviving an event, where other analytics may project failure. Our models optimize parts procurement, crew schedules, down times, and other maintenance activities. As a result, you extend asset life, increase your return on invested capital, and reduce maintenance and operating costs.
Unsupervised learning uses algorithms to find hidden structures and relationships in unlabeled data, without knowing what the possible outcomes could be. It’s good for finding clusters of similar items, detecting anomalous data, and performing multivariate analysis. For example, predicting asset failure times can be accomplished with unsupervised machine learning.
Reinforcement learning features a software agent that learns from the outcomes of the actions it takes to maximize future rewards. Different changes in the state of the environment are associated with different levels of rewards. Typically, its actions influence subsequent decisions, as the agent seeks the highest level of rewards over a given time period. Combined with techniques like stochastic optimization and queueing theory, this approach excels at optimizing real-time business processes.
Supervised learning creates a model where the outcomes are known. The system is trained using real or example data sets, creating a model from which it can then respond appropriately to new data.