Research & Best Practices

Predictive Maintenance and Machine Learning: Benefits and Methods

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Often discussed as a major part of the future of predictive maintenance and manufacturing technology, machine learning is one of the most valuable tools at manufacturers’ disposal today — and yet, remains misunderstood and underutilized. As machine learning becomes more commonplace, however — and more familiar — manufacturers can gain ever-increasing benefits, especially in predictive maintenance.

What is machine learning? It is a form of artificial intelligence in which existing data and conditions are used by a computer algorithm to “learn” and, thus, make predictions about future events, based on previous ones. Machine learning is unique in that the algorithm uses the data that is fed into it to model increasingly sophisticated predictions, without the programming of the algorithm changing. With the vast amount of data that can be collected by the technology used to enable predictive maintenance, machine learning holds great potential to offer massive benefits in this area.

Machine Learning and Predictive Maintenance of Machines in Manufacturing

Machine learning (ML) is an advanced form of artificial intelligence that relies on complex computer programming. At its core, however, ML relies on two primary factors to work:

  1. Data
  2. An algorithm

Data for machine learning comes in two main forms: historical data and continuous data collection (almost always collected by industrial sensors). Historical data is used to essentially get the algorithm up and running, and to begin its “training” to generate predictive models and increase accuracy. Continuous data improves the accuracy of the model in the form of more information. The algorithm is created to collect, process, and analyze data — essentially, to make sense of the massive amount of information available and to separate “signals” (salient data points) from “noise” (data points that are irrelevant to a predictive model). For data to be useful and for the algorithm to operate effectively, data must be “clean.” It must be organized, and it must be properly “described” for the algorithm to know what to do with it.

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For example, a collection of machine temperature condition data points by itself is not that useful — even if the data covers a period of time including standard operation along with breakdowns. The data must be associated with the events in question, so that the algorithm “knows” that a certain data point means “operating as expected” or “breakdown imminent” (or “breakdown in progress,” etc.). With data and an algorithm, various types of modeling for machine predictive maintenance are possible. The main model types are:

  • Classification modeling: Providing a Boolean (“yes” or “no”) answer, classification modeling is useful because it can generate a result with less data. In general, classification modeling is used to predict whether a machine will break down or remain operational during a given period. In predictive maintenance scenarios, this period will generally be short — perhaps a few hours from the present, or a day or two. This allows maintenance personnel to know whether they should plan repairs soon.
  • Regression modeling: Regression modeling is more complex and is used to predict “remaining useful life” (RUL) — the time remaining until the next failure. Regression modeling typically requires a larger data set with more failure events included (and properly identified) — but once this data is available, the ability to predict RUL is incredibly useful, allowing for more accurate maintenance planning and other benefits as well (described in greater detail in the following section).

Benefits of Machine Learning and Predictive Maintenance

Predictive maintenance with machine learning offers a wealth of benefits, such as:

  • Reduction in failures, downtime, and repair time: With predictive maintenance and machine learning, maintenance operations personnel gain unprecedented insight into equipment performance and expected future states, with a great degree of accuracy. This allows for maintenance planning to address issues before they lead to unexpected equipment failure, unplanned downtime, and inefficient emergency repairs. By preparing for maintenance and planning it for less disruptive times, numerous operational efficiencies are gained.
  • Lower maintenance costs: Predictive maintenance with machine learning can reduce maintenance costs across the board, in several areas. Planning for maintenance means that parts can be located or procured as needed, without expensive emergency ordering. The right personnel can be scheduled, so that extra costs need not be incurred. Further, less downtime means increased productivity — reducing or eliminating one of the biggest resource wastes in manufacturing.
  • Less spare parts inventory needed: Machine learning enables much more accurate inventory forecasting — especially with regression modeling involved. This allows for the inventory operation to keep fewer parts on hand at any given time since its need is now being predicted with much greater confidence. This enables a reduced inventory footprint and a more streamlined operation.
  • More accuracy in maintenance planning: Overall, machine learning helps facilities accurately plan their maintenance needs in personnel, equipment operations, inventory, scheduling and more — resulting in more efficient use of all resources.

Predictive Maintenance Solutions With ATS

With over three decades of experience in industrial maintenance, ATS offers predictive maintenance solutions that rely on advanced manufacturing technology including sensors for machine health monitoring, data analytics and prescriptive actions by reliability engineers. For more information, contact ATS today.

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