In this post, we explain predictive maintenance analytics from scratch. We’ll help you understand how predictive maintenance analytics can provide insights that can save you thousands of dollars.
Equipment downtime is one of the most costly problems for manufacturers. Picture this: what if you could predict with reasonable accuracy when equipment failure will occur based on data collected by IIoT (Industrial Internet of Things) devices? You can if you implement predictive maintenance analytics.
Key takeaways
- Predictive maintenance analytics uses real-time sensor data combined with historical patterns to predict equipment failures before they occur, potentially increasing equipment uptime by up to 20%.
- Machine learning models require high-quality training data from multiple sources including failure records, repair histories, real-time equipment monitoring, and asset metadata to make accurate predictions.
- Common IoT sensors for predictive maintenance include MEMS accelerometers for vibration analysis, current sensors for electrical monitoring, and pressure sensors for fluid systems monitoring.
- Organizations achieve the best results by combining predictive maintenance with other maintenance strategies rather than relying solely on predictive analytics, as the technology still produces false positives and negatives that can offset cost savings.
What is predictive maintenance analytics?
Predictive maintenance analytics is an analytical tool that allows you to predict equipment failure and prevent equipment downtime. It involves real-time monitoring of critical assets and comparing this real-time data with historical data to identify potential failure events. This helps prevent breakdowns and minimize maintenance costs.
Predictive maintenance (PdM) is currently the most advanced type of proactive maintenance. Its predecessors include preventive maintenance and condition-based maintenance (CBM). Out of the two, only CBM uses data and analytics. The technique involves condition monitoring to prevent unplanned downtime.
PdM differs from CBM because PdM uses aggregated sensor data, while CBM relies only on real-time data collected during monitoring.
“Leading executives have used IOT technologies to transform the way their companies service equipment. . . What could be more attractive than better uptime, greater predictability, and, presumably, lower service costs?”
McKinsey
How predictive maintenance analytics works
Unlike reactive maintenance, PdM is scheduled maintenance based on identifying equipment problems that could lead to failure. For this, PdM requires data and an algorithm for data analytics.
Here’s what you need for PdM and how it works:
- Technologies like machine learning (ML) and artificial intelligence (AI): ML and AI algorithms can learn the causes that lead to failure. Predictive maintenance solutions use predictive models powered by these technologies to analyze big data.
- Big data: Historical data helps the machine learning algorithm identify patterns that indicate looming failure. Your IoT sensors will continue to add data to the database. You’ll have to convert this data into structured data so the predictive models can perform data analysis to derive value and context from it.
- Real-time data: Continuously monitoring a piece of equipment requires collecting and analyzing real-time sensor data. Predictive models look for patterns and events that can cause failure based on the patterns observed in big data.
“As more companies implement IIoT solutions, data has become exponentially more important to the way we automate machines and processes within a production plant, including maintenance processes.”
Machine learning model for PdM tools and analytics
A machine learning model is only as effective as the data you train it with. Here’s what you need to keep in mind when collecting training data for the machine learning model that you’ll use for predictive analytics:
- Focus on quality: Data quality is the most critical consideration when collecting data. You’ll need a data scientist to monitor the quality and cleanse the data. Seeking help from a third party for data quality assurance goes a long way.
- The more data you have, the better: When collecting data, you’ll probably wonder when’s a good time to start using the data to train the model. Well, there’s no standard here. But as a general rule, the more data you have, the more accurate predictions your predictive maintenance system will make.
- Select the right data sources: Before you start collecting data, identify the equipment you want to monitor and the issues you want to avoid so that you can select the right data sources. It’s vital to install the right sensors on the right equipment.
“Predicting failures via advanced analytics can increase equipment uptime by up to 20%.”
Deloitte
Data sources for predictive maintenance analytics
Now let’s talk about where you can collect data. Below are the types of data sources you can use to collect data. Of course, these aren’t the only data types you can use but they are the most common.
Failure, repair, and maintenance data
Historical data on failure events and maintenance help train the algorithm. Failure events might be rare, but you can use events where you replace spare parts or detect an anomaly as a proxy for failure events.
Maintenance data, including data on repair activities, the condition of the equipment when it was repaired, and the components that had to be replaced are critical pieces of information. Unfortunately, collecting this data manually is next to impossible. Instead, consider using a computerized maintenance management system (CMMS) for easy data collection. CMMS with robust reporting features will provide you with real-time KPIs and metrics, like MTBF, MTTR, and OEE.
In-use equipment data
Predictive maintenance assumes that equipment condition degrades as it’s used. Collecting real-time sensor data allows for identifying a specific asset’s degradation pattern.
Start by connecting sensors and other monitoring equipment with a CMMS and let the database build over time. Once the algorithm is trained with this data, it can try to look for similar patterns in the data collected in real time.
In addition to identifying anomalies that can lead to failure, algorithms can also use real-time data to predict an asset’s useful life: how long an asset can be used before it requires repairs or has to be replaced.
Metadata
Metadata is a one-and-done data type—you only need to add it to the system once. In addition, metadata includes information like the model, date of manufacture, and technical specifications. These details help the system identify patterns among a narrower category of assets, potentially improving the accuracy of predictions.
Data collection devices for PdM analytics
Data is collected through various types of sensors. The sensors are mounted on the asset strategically to record relevant parameters, such as vibration and temperature. Here are examples of sensors you can use:
- Microelectromechanical sensors (MEMS): MEMS sensors are solid-state accelerometers that collect vibration data. Collecting vibration data can help detect issues related to lubrication, misalignment, etc. Vibration analysis can provide early warnings of machine malfunctioning and prevent downtime.
- Current sensors: Current sensors help prevent equipment damage caused by power spikes. This reduces the cost of repeatedly replacing motors by allowing technicians to troubleshoot the problem.
- Pressure sensors: Pressure sensors help collect data from components used by fluid or gas. The sensors can trigger an alarm when the pressure level breaches a high or low limit.
These are just a few examples of IoT sensors you can install at your plant. These sensors collect data and transmit it to your CMMS. Maintenance teams can monitor assets using this data and, when necessary, address the root cause before the problem leads to failure.
When to use predictive maintenance analytics?
PdM can translate to significant savings when deployed under the right circumstances. However, like all technologies and techniques, predictive maintenance has limitations. The most noteworthy shortcoming is the possibility of false positives and negatives. These can wipe out the savings predictive maintenance helps generate to a great extent.
Deloitte explains that predictive maintenance should be reserved for circumstances with greater risk or lack of predictability. In other situations, CBM or advanced troubleshooting (ATS) may yield more cost savings. Both rely on the same technologies as predictive maintenance to extract and analyze data, though they use the data differently than PdM.
Dirk Claessens, the managing director at IBM for the Royal Dutch Shell account, explains that PdM is still in the proof of concept stage. People are starting to derive much value from it, but most aren’t going all-in just yet.
So for most of us, using multiple techniques to minimize downtime makes sense, at least for now.
Modernize your maintenance strategy with a PdM program
PdM helps optimize your maintenance efforts by facilitating data-driven decisions. It’s important first to assess if investing in PdM will generate an ROI that justifies the investment. Investing in PdM can improve profitability and efficiency if you’re losing a lot of money to asset failure.
Installing sensors is a one-off task, but data collection is ongoing. That’s why having a CMMS to manage data is critical before starting with PdM.
Get MaintainX for your PdM program
MaintainX receives data from sensors and automatically adds it to a database. Your PdM model can retrieve data from MaintainX as needed, streamlining the process from data collection to analytics.
MaintainX also includes capabilities like work order management and asset management. For example, you can create work orders when PdM models suggest an asset requires maintenance using the same data collection app.
Try MaintainX today to see how it can help you streamline maintenance and PdM.
Predictive maintenance analytics FAQs
How does predictive maintenance analytics differ from traditional preventive maintenance in terms of scheduling approach?
Traditional preventive maintenance operates on fixed time intervals or usage metrics, performing maintenance tasks whether they're needed or not—such as changing oil every 3,000 miles regardless of actual condition. Predictive maintenance analytics, by contrast, uses real-time data from sensors and equipment monitoring to determine the actual condition of assets and predict when maintenance will be needed.
This data-driven approach means maintenance occurs only when indicators suggest an impending failure, eliminating unnecessary service while preventing unexpected breakdowns. The shift from calendar-based to condition-based scheduling represents a fundamental change in maintenance philosophy.
What data sources feed into predictive maintenance analytics systems?
Predictive maintenance analytics draws on multiple categories of data to assess asset health. Common sources include sensor and IoT data that monitor real-time equipment conditions such as temperature, vibration, and pressure; historical maintenance records documenting past failures, repairs, and performance patterns; and operational data like production schedules, usage intensity, and environmental conditions.
Depending on the system and industry, organizations may also incorporate external data such as weather conditions, manufacturer specifications, or supplier information. These data streams work together to create a comprehensive picture of asset health, and the integration of multiple sources allows analytics systems to identify patterns and correlations that wouldn't be visible from any single data type alone, enabling more accurate failure predictions.
What specific machine learning techniques are used for analyzing equipment failure patterns?
There are several machine learning approaches for predictive maintenance: regression analysis to predict remaining useful life of components based on degradation patterns; classification algorithms that categorize equipment states as normal, warning, or critical; and anomaly detection systems that identify unusual patterns in sensor data that may indicate developing problems.
These techniques process historical and real-time data to recognize subtle indicators of impending failure that human observers might miss. The algorithms continuously learn and improve their predictions as they process more operational data over time.
How do predictive maintenance analytics systems calculate and prioritize maintenance actions?
Predictive maintenance analytics systems use risk-based prioritization that considers both the probability of failure and the potential consequences. The systems analyze failure likelihood based on current equipment condition data and degradation trends, then weigh this against factors like criticality to operations, safety implications, and cost of downtime. This creates a prioritized maintenance schedule where high-risk, high-impact equipment receives immediate attention while lower-priority items are scheduled appropriately. The approach ensures maintenance resources are allocated to maximize operational reliability and minimize total cost, rather than treating all equipment equally.
What implementation challenges do organizations face when adopting predictive maintenance analytics?
Organizations face several key challenges when implementing predictive maintenance analytics: the initial investment in sensors, connectivity infrastructure, and analytics software can be substantial; integrating data from legacy equipment that wasn't designed for digital monitoring requires retrofitting or workarounds; developing the necessary data science expertise or training existing maintenance staff to interpret analytics outputs takes time; and overcoming organizational resistance from maintenance teams accustomed to traditional approaches requires change management.
Additionally, organizations must establish data quality standards and governance processes to ensure the analytics systems receive reliable inputs, as poor data quality directly undermines prediction accuracy.
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