Machine Learning

Powerful algorithms harnessed for maximum operational efficiency

Machine Learning for Assets

Our machine learning models and other advanced analytics were created specifically for asset-intensive industries; they can predict and optimize asset health and related operations into the future without loss of accuracy or confidence. This translates directly into greater cost savings and higher ROI. Our analytics applications provide insights, recommend actions, and make decisions that are too complex for people to make in a timely manner, or at all.

 

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.

Cutting Edge Machine Learning Delivers Results

Machine learning is a type of artificial intelligence that can improve its models without explicit programming.  Machine learning excels when there are too many variants to create an explicit model for each, or too many unknown variables to accurately project what combination of factors may affect an outcome. SpaceTime’s innovative machine learning models combine sophisticated algorithms to deliver unprecedented insight into your assets and operations.

Unsupervised Learning

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

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

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.

Deep dive: Machine Learning Model for Predicting Asset Failure white paper

From Innovation to Insights

SpaceTime’s strong data science skills have created innovative machine learning approaches to finding the hidden value in our customers’ data, uncovering elusive insights. The unique combination of these and other models delivers high confidence in their predicted outcomes or prescribed results.

Bayesian Networks

SpaceTime’s probabilistic models represent the seemingly random variables and related dependencies that can lead to asset failure. The strength in the flexible models lies in their extensibility to multiple asset types in a wide variety of industries.

Stochastic Optimization

Uncertainty makes finding optimal decisions impossible for humans, and difficult even for reinforcement machine learning applications. These variables may be unpredictable, but they often follow laws of probability. Using probabilities rather than definitive values for these unpredictable variables — an approach known as stochastic optimization — gives reinforcement learning applications a way to find optimal solutions that allow for the level of uncertainty.

Hidden Markov Models with Topic Mixtures

SpaceTime’s hidden Markov models are sophisticated machine learning tools that capture a rich family of probability distributions that more accurately model real-world phenomena. Our approach allows a sequence of possibly nonstationary mixture models that blend many simple distributions together to create a more complex and highly realistic probability distribution function. The result is a powerful representation of time-series data for analysis.