Predictive Maintenance Using Machine Learning | Part 2

In the last article, we discussed about predictive maintenance solutions and the key components required to develop a solution suitable for a business. We’ll continue to discuss more about the modeling techniques and the solutions we offer at Predactica.

Modeling Techniques

Predictive Maintenance problems can be formulated in many ways and can be different based on specific business needs. The type of models applied is dependent upon the type of problem it is trying to address. Broadly we have the following modeling techniques in Predictive maintenance.

predictive maintenance using machine learning

  1. Binary classification – One of the most common types of predictive maintenance problem is if a machine is going to fail in certain future period of time. For example, if a machine is likely to fail in next 7 days or one month can be answered with a binary classification modeling technique, where the answer is YES or NO.
  2. Regression – Regression type of modeling techniques are commonly used in Predictive maintenance to predict the remaining useful life (RUL) of a machine, that is the amount of time left before the next failure. This is useful in planning resources and inventories so that impact of production loss is minimal during the predictive maintenance.
  3. Multi-Class classification – multi-class classification is a type of predictive technique where instead of answering Yes/No for a binary classification problem, it provides multiple choices to the business question. Examples are multiple reasons to the failures and predicting the right failure reason. Similarly, there could be multiple time periods as multiple choices for answering a prediction question like which future period will the machine most likely to fail.
  4. Anomaly Detection – Anomaly detection is the type of classification problem that alerts when something anomalous behavior detected in normal trend of historical behavior. This is an important analytical tool to identify and explain the abnormal patterns in the data used in Model training.

The above modeling themes are common but are not the only ways to formulate the predictive maintenance problems and certainly takes business inputs to arrive at the right approach to a specific problem.

Overview of the Machine Learning Process

The Predictive maintenance ML process is no different than the common Data Science process flow followed in any predictive use cases. The ML process for Predictive maintenance involves collecting the various datasets for pre-processing, performing Exploratory data analysis (EDA) and Feature engineering activities before the Models could be trained and evaluated for business recommendation. Time series-based forecasting in climate sensitive environments like with wind-turbines could require custom feature engineering techniques to identify the trends and seasonality in the life of the subject. Feature selection and feature engineering techniques are use case sensitive and requires domain knowledge to formulate the strategy in the right way. Once model is trained the model performance can be evaluated based on several metrics such as Accuracy, Recall, Precision and others in case on Classification models and MSE/RMSE metrics in case of regression.

Predactica Solution

Predactica’s ML solution for Predictive Maintenance is a custom solution template designed for Predictive Maintenance use cases and could be customized and packaged for specific industry use cases. The solution has an intuitive workflow that encapsulates custom data preprocessing, feature engineering and modeling techniques to prepare the data along with intelligent recommendations from the ML engine. The intuitive Auto ML feature allows to train multiple models and evaluate them for side-by-side comparison.

Predactica’s AI/ML enabled solution provides early warning notification and diagnosis of equipment issues days, weeks, and months in advance of failure. This could help organizations reduce equipment downtime, enhance workforce management, and reduce operations and maintenance expenditure.

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