Unplanned downtime reduces a plant's production and profitability.

A robust predictive maintenance approach is required to mitigate these losses. Predictive maintenance predicts when a vital asset will fail so that repairs may be performed without interfering with production or maintenance activities.

This is easier said than done. Forecasting failures need the study of large data sets, which may be challenging and time-consuming, with hundreds of potential changes occurring. A facility needs a predictive maintenance strategy supported by the right data technologies to increase its effectiveness and extend its early-warning capabilities.

Machine Learning

Machine learning (ML) is a data technology that improves a data software program's capacity to expect future events, such as impending asset breakdowns, with no human input after the initial setup phase. Forecasts are based on historical asset performance under various situations, such as seasonal fluctuations, startups, and shutdowns. It is a powerful tool for predictive maintenance, which refers to using data and analytics to predict when equipment failure might occur and take corrective action before that failure occurs.

During setup, the ML program analyses data sets and process parameters from various sources to identify patterns or warning signals of potential errors the human eye might miss. The ML system is so good at this task that one research discovered it could forecast equipment breakdowns with up to 92% accuracy.

By analyzing sensor data from machinery, ML algorithms can learn to recognize patterns with a high likelihood of future failures. These algorithms can then predict when maintenance will be needed, allowing maintenance teams to perform maintenance before the equipment fails.

These early predictions carry potent possibilities. Machine learning may assist in problem resolution, determining the underlying cause of asset failure, increasing asset availability, increasing production output, reducing unexpected downtime, and lowering operating and maintenance expenses.

ML algorithms can also be used to identify the root causes of failures, helping maintenance teams to address the underlying issues that lead to equipment failure in the first place.

Types of Machine Learning

There are four types of machine learning. Each sort of machine learning uses data, whether labeled, unlabeled or a combination of the two.

Labeled Data
Raw data is accompanied by context information about the data's features and attributes. Labeled data tells ML what it's looking for and what data to use, increasing its efficiency and accuracy. Yet, it takes time to construct and is prone to human mistakes. This might result in decreased data quality or algorithm miscalculations.

Unlabeled Data
Raw data is not accompanied by contextual information. Unlabeled data is affordable and accessible, and when applied to ML, it forces the system to discover its classifications. Because it can see overlooked or underutilized patterns, the categories ML creates are often accurate. Unlabeled data forces ML to draw inferences that may or may not lead to the intended result, forcing you to reconsider your selected data and specified parameters.

Supervised

The most popular sort of machine learning is supervised learning, which uses labeled data. The system understands what information will lead to the desired result by providing labeled data. Because this ML operates with historical data, the results are unbiased.

During setup, the algorithm is given this data and told the desired outcome, such as predicting future failure events. The algorithm will learn to interpret the data and perform this procedure while looking for potential improvements to the system.

Supervised machine learning helps separate labeled data into two categories, selecting between two or more classification categories, making predictions and connections between data with many independent values, and connecting prognoses from many ML models to create one accurate forecast.

Unsupervised

Unsupervised ML systems use unlabeled data. The system takes this data and sorts the information based on identified patterns and connections to arrive at the most logical inferences. These conclusions are then utilized to discover meaning in the data and determine when an asset may fail.

Unsupervised machine learning helps group data based on similarities, find abnormalities within a data collection, recognize concurrent data points, and condense the number of data variables.

Semi-Supervised

Semi-supervised machine learning systems strike a compromise between supervised and unsupervised learning methods. The system is given a starting set of labeled data to learn the linkages and correlations applied to all following unlabeled data. In this way, the ML system is given an initial direction but can advance by itself, allowing it to discover more efficient methods of arriving at the desired output.

Semi-supervised machine learning helps identify abnormalities with limited examples and apply labels to large unlabeled data sets.

Reinforcement

Reinforcement ML systems learn through trial and error using unlabeled data. While it still has a choice over what steps are done, the system is designed with positive and negative rewards to urge it to reach a specific aim. As a result, it is encouraged to learn from its mistakes and grow more through the process.

Reinforcement machine learning helps divide limited resources to reach a defined goal and teaches robotic machines to complete tasks in the physical world.

Steps to Establishing a Machine Learning System

Once you've determined which ML system suits your plant's requirements, you can begin the setup procedure. While the process is straightforward, each step must be done to the highest standard possible to guarantee correct data entry and output identification. These are necessary for the system to guide and produce accurate results.

1. Create a model of the asset you wish to test. Include all process and manufacturing equipment characteristics.
2. Import historical operational data for the asset into the model. This is referred to as the "training data set," It should include one year of data to account for seasonal functional changes.
3. Choose which data to include and which to reject from the training data set. This data will create an operational matrix that specifies how the asset should function at any given time.
4. Deploy the machine matrix. This matrix will start monitoring the asset to forecast when it will leave the established parameters and start failing. The program will create an alert if a significant enough deviation is identified. A team member then evaluates this alert.

When an alarm is sent, and an evaluation is completed, there are three possible outcomes:

1. The alert is correct, and action should be taken to correct the problem.
2. The alert needs more research or operational data. The signal and all associated parameters will continue to be monitored for alert conditions.
3. The alert is a false positive. The model will need to be retrained with more operational data and redeployed.

Preventive vs. Predictive Maintenance

Preventative and predictive maintenance are two main maintenance methods businesses, and manufacturers use to decrease equipment downtime and enhance operations. Both methods aim to cut machine downtime and get the highest Overall Equipment Effectiveness (OEE) to cut maintenance costs.

Preventive Maintenance is a simple and easy method that involves performing regular maintenance of machines in each set of time. This type of maintenance is based on time, usage, or specific events, such as the number of hours of operation, calendar dates, or the number of parts produced.

Machine learning algorithms and other predictive technologies, a data-driven maintenance strategy, are used in predictive maintenance to identify possible equipment failures before they happen. This type of maintenance involves collecting data from sensors and other sources and using analytics to identify patterns and anomalies that state equipment problems.

To choose between predictive and preventive maintenance, we must consider several factors: type of equipment, the industry, specific maintenance requirements, and reducing unnecessary maintenance costs.

Preventive maintenance is often more successful in reducing downtime in equipment with a known failure pattern or when the cost of failure is modest. Predictive maintenance is more successful in reducing downtime in equipment with an unexpected failure pattern or a high failure cost.

Solutions

ML can improve the efficiency and effectiveness of maintenance operations, helping to reduce downtime, extend the lifespan of equipment, and save money on maintenance costs.

Predicting when an asset may break allows us to take remedial action and cut production delays and unexpected downtime. Predictive maintenance tactics provide a chance to do so while establishing a manufacturing culture based on data and established maintenance approaches.

Although successful, completing every stage of a predictive maintenance approach can be difficult, especially when time is limited and human error is present. Even so, collaborating with a high-quality machine learning system can improve our efficiency and expand our early-warning capabilities, resulting in a more powerful, leaner facility.

With Le Price International’s UE 4Cast, you can save time, bearings, and critical parts and achieve your reliability and profitability goals. It is a 24/7 Ultrasonic Monitoring System with an intelligent alert system; it records data and sounds, sends data and files to DMS software, can set up different alarm levels, has an ethernet connection, is flexible, and you can request an online demo to get a quotation.

Le Price International also offers FailureProtect™ Service, a Condition-Based Monitoring (CBM) subscription service that is an end-to-end solution to your predictive maintenance need for a more reliable and safer operation. By subscribing, there is no need for an outright sale. We will provide end-to-end monitoring, complete with diagnosis and solutions.

Sources:

Moll, B. (n.d.). Machine Learning for Predictive Maintenance. ReliablePlant. Retrieved from https://www.reliableplant.com/Read/32281/machine-learning-predictive-maintenance

SHETTY , N. I. D. H. I. (2023, March 6). Preventive vs. Predictive Maintenance: Which is More Effective for Reducing Downtime. VIKING ANALYTICS. Retrieved from https://vikinganalytics.se/preventive-vs-predictive-maintenance-which-is-more-effective-for-reducing-downtime/