Predictive Maintenance Analytics: Maintenance Manager’s Guide

Many organizations report the cost of downtime at $100,000 to $300,000 per hour or more. At that rate, taking measures to predict and prevent breakdowns is an important investment. And with the power of machine learning, big data, and condition monitoring at your fingertips, maintenance teams can use predictive maintenance analytics to increase equipment uptime by up to 20%. This is a key component of digital transformation within maintenance operations.

We’ll share all the details of leveraging predictive maintenance analytics, and show you how it’s done.

What is predictive maintenance analytics?

Predictive maintenance analytics is a proactive approach to equipment maintenance that combines data analysis and machine learning to forecast when machinery might fail. Instead of relying on scheduled maintenance or reacting to breakdowns (also knowns as reactive maintenance), it aims to predict and prevent failures before they occur. This is achieved by continuously monitoring equipment conditions using various IoT sensors that collect data on factors like vibration, temperature, and pressure.

This data is then processed and analyzed using sophisticated algorithms to identify patterns and anomalies that indicate potential issues. By recognizing these early warning signs, organizations can schedule maintenance only when necessary, minimizing downtime, reducing maintenance costs, and extending the lifespan of their assets.

Essentially, it allows businesses to shift from a reactive to a predictive maintenance strategy, leading to greater efficiency and operational reliability.

How does predictive maintenance analytics work?

Predictive maintenance analytics operates through a multi-step process:

Data collection

In a predictive maintenance program , sensors are used to collect data on assets. There are different types of sensors that can measure temperature, vibration, pressure, and more. Some of the sensors commonly used include:

• Infrared analysis sensors are used specifically to compare the difference in temperature of a component over time. These sensors can be used to monitor bearing temperatures in large motors, measure piping conditions, calculate process temperatures, estimate plumbing conditions, verify solar panel conditions, evaluate the condition of electrical components, and more.
• Vibration analysis sensors measure parts vibration to detect faults related to misalignment, mechanical looseness, gear defects, lack of lubrication, resonance, rubbing, cavitation, corrosion, and more.
• Ultrasonic analysis microphones use a sensitive microphone to pick up high-frequency sounds and detect the need for electrical inspection, steam trap maintenance, improved lubrication, leak maintenance, mechanical inspection, electric arc flash detection, and valve testing.

All sensors must be connected to CMMS software. Through the power of the Internet of Things (IoT), the software uses real-time sensor data and predictive models to generate maintenance notifications.

This way, maintenance staff can monitor the health of a machine and act on the root cause of an issue before it becomes a catastrophic breakdown.

Data processing

Before any predictive algorithms or models can be used, all collected data must be gathered, structured, aggregated, and stored so that it is accurate and usable. This initial stage constitutes around 70% of most data science projects.

These datasets have traditionally been stored and analyzed independently of one another. However, with the power of the Internet of Things (IoT) technology and other advances, it is now possible to connect the systems that measure and collect data, and those that perform an analysis. In the end, this offers a more complete picture of the asset lifecycle.

Model development

Machine learning algorithms are trained on historical and real-time data to create predictive models. These models learn to identify patterns and correlations between equipment conditions and potential failures. Common techniques include regression analysis, time series analysis, and neural networks.

Anomaly detection

The predictive models are used to monitor the equipment’s current condition and detect anomalies or deviations from normal behavior. These anomalies can indicate early signs of potential failures.

Failure prediction

Based on the detected anomalies and the learned patterns, the models analyze and predict the likelihood and timing of future failures.

Maintenance scheduling

The predictions are used to schedule maintenance tasks proactively, ensuring that repairs are performed before failures occur. This helps to minimize downtime and reduce maintenance costs.

Continuous improvement

The system continuously monitors the equipment and refines the models based on new data and maintenance outcomes. This feedback loop improves the accuracy of the predictions over time.

Predictive maintenance analytics benefits for specific industries

Predictive maintenance analytics offers significant benefits across various industries, tailored to their specific needs. Here are just a few examples:

Manufacturing

Predictive maintenance analytics revolutionizes manufacturing by minimizing unplanned downtime. In production lines, even a short stoppage can lead to significant losses. By predicting when equipment will fail, manufacturers can schedule maintenance during planned downtimes, maximizing Overall Equipment Effectiveness (OEE).

Furthermore, predictive analytics optimizes spare parts inventory, impacting the supply chain. Instead of stocking excessive components, companies can predict when replacements are needed, reducing storage costs. Finally, by ensuring machinery operates within optimal parameters, manufacturers can improve product quality and consistency, reducing defects and waste.

Energy (oil and gas, renewables)

The energy sector, particularly oil & gas and renewables, faces unique challenges due to the critical nature of its infrastructure and often remote locations.

Predictive maintenance systems enhance safety by detecting potential failures in pipelines, wind turbines, and other critical assets, preventing accidents and minimizing risks. This proactive maintenance approach also minimizes environmental impact by preventing leaks and spills that can cause ecological damage.

In remote locations, optimizing maintenance schedules reduces operational costs, as travel and logistics for repairs are expensive. Predictive analytics allows for targeted interventions, saving time and resources.

Transportation (aviation, rail, automotive)

Predictive maintenance analytics plays a crucial role in increasing passenger safety by predicting and preventing equipment failures in transportation such as aircraft, trains, and fleet. By monitoring critical components, potential issues can be addressed before they lead to accidents.

Moreover, this approach reduces delays and disruptions by proactively addressing maintenance needs, improving operational efficiency.

Extending the lifespan of critical components also reduces replacement costs, making transportation more cost-effective.

Healthcare

Healthcare facilities relies on the continuous operation of critical medical equipment. Predictive maintenance ensures the reliability of these devices, improving patient care and safety.

Minimizing downtime of essential equipment like MRI machines and ventilators is crucial for timely diagnoses and treatments.

Furthermore, the high cost of medical equipment makes predictive maintenance a valuable tool for reducing maintenance expenses.

By predicting failures, healthcare providers can schedule repairs efficiently, extending the lifespan of their assets.

Utilities (water, electricity)

Utilities like water and electricity providers can significantly benefit from predictive maintenance analytics. By predicting failures in distribution networks, power outages and water supply interruptions can be minimized. This proactive approach ensures a more reliable service for consumers.

Furthermore, predictive analytics helps optimize the maintenance of aging infrastructure, extending its lifespan and reducing the need for costly replacements. By detecting and addressing leaks and other inefficiencies, utilities can improve operational efficiency and reduce waste, leading to a more sustainable and cost-effective service.

Big data and predictive maintenance analytics

Big data refers to large and complex data sets that grow at ever-increasing rates. Within the context of predictive maintenance, it means that as sensors keep sending real-time information on asset condition, the amount of data on hand grows at a constant pace.

In the field of maintenance, the term big data often refers directly to the use of predictive analytics. And that’s because big data is the foundation of predictive maintenance solutions.

Two vital components are necessary to perform big data analytics for maintenance: building a database, and applying predictive analytics models to the data.

Data requirements for predictive analytics

Predictive modeling applies statistics to a machine’s historical data to predict outcomes such as potential machine breakdown. However, the accuracy of any prediction depends on three things: relevance, quantity, and quality of data. These factors directly impact the metrics used to assess predictive maintenance effectiveness.

Relevance

When generating “training” data — that is, historical data used to develop predictive models— the data must relate directly to the predictions you want the model to make. This means that you must have an idea of the types of predictions and equipment failures you want the algorithm to detect.

For instance, if the goal is to predict the failure of a traction system, the training data has to encompass all the different components for the traction system.

Designing prediction systems around specific components and their failures rather than larger systems will help provide more specific and actionable insights.

Quantity

Another important aspect to consider when building the “training” data is the amount of information needed to make accurate predictions.

Since every piece of equipment is different, there is no one definitive answer to the question of how much data is enough data. However, the more data about specific equipment you have, the more accurate your predictions will be.

Keep in mind that there are failures that are rare in some types of equipment, which means that the “training” data won’t gather this event unless you make sure to include it.

Quality

Quality is perhaps the most critical element of building historical data. Data is probably your most valuable intangible asset, so making sure it is accurate will be worth the effort. Data scientists regularly verify and “cleanse” data before using it to train or develop a predictive model.

Make sure to incorporate data quality fundamentals into your PdM project plan such as maintaining an audit trail as “training” data is being prepared, having an assigned individual responsible for data quality, and obtaining independent quality assurance.

Sources for condition-based machine data

There are multiple sources for historical machine data. The main data sources include failure history, maintenance history, condition monitoring data, and equipment metadata. Learn how to collect and use machine data.

Failure history

As you know, most equipment doesn’t have unplanned downtime often. Therefore, it becomes challenging to obtain data directly from failure events.

The data used to predict failures tends to come from a component’s normal operations. When building predictive models, algorithms use maintenance records and parts replacement history to find failure events. For this reason, data that includes detection of anomalies can also help predict potential failures.

Maintenance history

Maintenance data history is a vital source of useful data. This data includes details about spare parts used, components replaced, repair activities performed, equipment condition at the time of the repair, and more.

Every maintenance activity performed on an asset can contribute to the algorithm’s development of degradation patterns. Failing to include this vital information in the training data can lead to misleading model results. This is crucial for condition-based maintenance.

Machine operating conditions

Real-time data from condition-monitoring sensors indicate an asset’s performance and operation condition. A key assumption in PdM is that a machine’s health status degrades overtime during its routine operation, and real-time condition data helps identify those patterns..

With historical data, artificial intelligence can determine characteristics that capture patterns of aging as well as any anomalies that indicate deterioration.

Long-term data is required for the algorithm to learn the failure and non-failure patterns over time. Based on this information, the algorithm learns to predict how many more units of time a machine can continue to work before it needs to be replaced.

Static feature data

Different from time-based data, static data is information about specific features and characteristics of each asset. Some examples include:

• Manufacturer
• Model
• Manufactured date
• Start date of service
• Control system location,
• Other technical specifications

Adding this type of information to the data used to make predictive models helps the algorithm make more exact predictions. It can also be used to construct a digital twin of the asset, for more accurate modeling.

Leverage PdM analytics to streamline decision making

Predictive analytics can be a game-changer for your maintenance team. But the implementation can be challenging if you don’t have the right resources to do it.

The first step is determining whether your organization is suited to this type of investment. And if unplanned breakdowns represent large losses for your business, the high ROI will be worth it.

When problems can be predicted in advance, they become solutions. Predictive maintenance strategies allow teams to lay out a plan to action with minimal effect on production and very little cost to the operation.

Limble CMMS connects a complete predictive maintenance module with other crucial modules such as work order management, preventive maintenance management, enterprise asset management, spare parts inventory, and more.

To learn more about how Limble can help you, book a demo or start a free trial today.

FAQs

What’s the difference between predictive vs. preventive maintenance?

Preventive maintenance is time-based or usage-based, performed at regular intervals regardless of the asset’s actual condition. Predictive maintenance, on the other hand, is condition-based, using data to predict when maintenance is needed.

How does predictive maintenance differ from reactive maintenance?

Reactive maintenance is fixing equipment after it breaks down. Predictive maintenance aims to prevent breakdowns before they occur.

What are typical use cases for predictive maintenance?

Typical use cases include monitoring critical machinery in manufacturing, predicting failures in transportation systems, optimizing energy infrastructure maintenance, and ensuring the reliability of medical equipment.

Can predictive maintenance be used for corrective maintenance?

Predictive maintenance helps inform corrective maintenance by identifying potential issues early, allowing for planned corrections instead of emergency repairs.

How do I choose the right solution for predictive maintenance?

When choosing a predictive maintenance software, consider factors like the types of assets you need to monitor, the available data sources, the complexity of your operations, and your budget. You may also want to review a relevant case study.