The Role of Predictive Maintenance in Achieving World-Class OEE with IIoT

The Role of Predictive Maintenance in Achieving World-Class OEE with IIoT | SPONSORED

OEE (overall equipment effectiveness) is a productivity and performance metric that helps find new inefficiencies and unlock the full potential of manufacturing. OEE helps manufacturers understand how well a machine, a manufacturing process, or a line is running, as well as its yield and efficiency. By improving OEE scores, organizations can optimize productivity, improve efficiency and product quality, and boost profit.

Predictive maintenance is a key transformative technology for OEE, which we’ll discuss later. It helps organizations forecast equipment failures before they occur, minimize disruptions, optimize asset performance, and ensure consistency in production output. It allows manufacturers to plan efficiently and reduce product variability and financial losses. By increasing availability, performance, quality, and utilization, predictive maintenance helps improve OEE score.

Effective predictive maintenance is not possible without key technologies like the industrial internet of things (IIoT), real-time analytics, and machine learning (ML), as we’ll see throughout this article. These technologies help collect sensor data, rapidly analyze it, identify patterns, and forecast potential failures in real-time. But before we go into detail, we’ll discuss OEE, its components, challenges, and how to use InfluxDB 3 to optimize OEE performance and predictive maintenance.

Understanding OEE and its challenges

The main components of overall equipment effectiveness (OEE) are availability, performance, and quality.

• Availability: Measures how the planned production time is utilized
• Performance: Represents the running speed of a manufacturing operation compared to its capacity (rated speed)
• Quality: Seeks to determine what number of products, out of the total number produced, meet the set quality standards

OEE score is the product of these three contributing factors, calculated by multiplying availability × performance × quality.

But what causes low OEE? The main causes of OEE losses include:

• Unplanned downtime halting production, equipment breakdowns, set-up time, and planned stops due to preventive maintenance or changeovers.
• Slow cycles due to machine underutilization and unregistered stops.
• Defects in the manufacturing process leading to scrap or product rework, affecting quality and increasing production costs.
• Inefficiencies due to inadequate maintenance, set-up delays, small stoppages, poor scheduling, and poor changeover processes.

Traditional maintenance strategies take a reactive or preventive approach that offers a low investment cost but exposes you to high production loss. If you’re after world-class OEE, you must implement proactive strategies such as predictive maintenance that allow you to find potential failures and take action before they affect production.

You need to implement proactive strategies such as predictive maintenance that will allow you to find potential failures and take action before they affect production.

How predictive maintenance transforms OEE

Predictive maintenance (PdM) improves OEE by transforming its three core components in the following ways:

• Ensuring availability: Predictive maintenance reduces unexpected downtime by detecting early warnings of equipment failure before they happen. By flagging issues early, PdM helps cut unplanned downtime and keeps production lines running most of the time.
• Optimizing performance: PdM optimizes equipment efficiency through real-time monitoring and anomaly detection. It tracks machine temperatures, speed, vibrations, etc., in real-time to detect unusual patterns that indicate inefficiency. It then alerts technicians to take action, keeping machines running at peak performance.
• Ensuring quality: PdM helps detect process deviations that could lead to defects in real-time. Addressing defects before they affect production ensures fewer reworks and reduced product wastage. As a result, a manufacturer can maintain consistency in quality standards and production output.

Key technologies enabling predictive maintenance in IIoT

Sensors & Edge Computing

Automated machine vision systems inspect and collect vibration, temperature, pressure, and other machine health data in real-time. Edge IIoT devices use smart sensors to detect potential issues faster while keeping interruptions at a minimum and productivity at optimal levels.

Real-Time Data Processing

Time series databases (TSDBs) handle high-frequency sensor data. These databases provide continuous query functionality that allows the analysis of high-frequency data streams in real-time. TSDBs integrate with stream processing platforms or have built-in stream processing capability, facilitating the analysis of new data as it’s ingested.

Machine Learning and AI

Predictive maintenance leverages AI and ML algorithms to learn from historical data. It analyzes data from monitoring systems and IIoT sensors to identify patterns. Insights from this analysis can help forecast potential machine failures early, before they impact production, and optimize maintenance schedules. Motion sensors collect energy consumption data, and AI/ML algorithms analyze this data to optimize energy consumption by identifying inefficiencies and predicting consumption trends.

Cloud & Edge Analytics

These technologies help balance on-premise and cloud-based analysis for fast, efficient decision-making. The cloud provides scalable computing power and storage resources, allowing the handling of large amounts of data. Cloud-enabled predictive maintenance systems also allow remote monitoring to assess the condition of manufacturing equipment off-site. Edge analytics ensure real-time data analysis on IIoT devices. This enables real-time monitoring, reduces latency, and facilitates rapid issue response.

Predictive maintenance leverages AI and ML algorithms to learn from historical data.

The role of InfluxDB 3 in predictive maintenance and OEE optimization

InfluxDB 3 provides the following capabilities ideal for OEE and predictive maintenance optimization:

Handling High-Velocity Sensor Data

InfluxDB 3 is built to ingest and query large volumes of time series data in real-time. Its data storage engine, InfluxDB IOx, is optimized for time series data. It leverages the FDAP stack, allowing the processing of millions of data points per second without affecting performance. This enables real-time sensor data analysis, monitoring, and rapid decision-making.

Storage and Scalability

You need efficient storage and scalability to manage high-cardinality industrial data streams. InfluxDB 3 provides efficient compression and high-cardinality support for diverse industrial data streams. Its cloud-native database engine, InfluxDB IOx, provides unlimited scaling and supports the analysis of high-cardinality data without compromising performance. It stores data in compressed Parquet files to reduce storage footprints and leverages storage in Amazon S3 to reduce storage costs.

Integration with ML & Analytics

InfluxDB 3 seamlessly connects to Grafana, enabling you to turn metrics, logs, and trace data into insightful visualizations and graphs in real-time. Integration with Apache Iceberg enables zero-ETL, no interoperability, and zero-copy data sharing with existing data warehouses and lakehouses, allowing access to historical data for real-time, large-scale analytics. Its support for AI/ML enables real-time access to data warehouses and data lakes and the building of predictive models that can identify patterns, detect anomalies, and optimize maintenance schedules.

Fast Query Performance

InfluxDB 3 enables instant insights into machine health, reducing reaction time to potential failures. InfluxDB 3 queries are fast, delivering sub-second query response times on leading-edge data and up-to-the-second insights. This is due to its use of a high-performance query engine, Apache DataFusion, which is built in Rust.

Steps to implement predictive maintenance for better OEE

1. Assess your current OEE and maintenance strategy. Analyze the current OEE scores to understand the main causes of inefficiencies. Evaluate maintenance logs, downtime records, and production metrics to understand failure patterns. After this, define clear improvement targets, such as reducing downtime, defects, or increasing throughput.
2. Deploy IIoT sensors to monitor equipment health in real-time. These may include temperature, vibration, and humidity sensors that capture health indicators that signal wear or impending failure, like unusual vibrations or equipment overheating. Prioritize IIoT sensors with edge computing capability, which allows local data processing and provides near-instant predictive insights.
3. Use a time series database to store and analyze data. Time series databases like InfluxDB 3.0 can sufficiently handle the high-velocity, time-stamped data that IIoT devices generate. They ingest data in real-time via protocols like MQTT. Then, they organize the data with tags for high-cardinality support.
4. Train ML models on historical data to predict failures and optimize maintenance. Historical data contains patterns based on past failure events, such as temperature rises before a motor stalling. ML models learn from this trained data, helping to identify failure precursors and using anomaly detection to flag unusual behavior.
5. Implement automated alerts and workflow integration to trigger proactive actions. Automated workflows close the loop on data insights and insight action. Configure alert triggers that automatically send notifications when a set threshold is attained. Integrate these alerts to trigger workflow automation. This will help assign tasks to technicians and schedule maintenance automatically.
6. Continuously improve your predictive maintenance strategy with data-driven decision-making. Regularly evaluate the accuracy of ML predictions against actual outcomes. Refine the models with new data to reduce false positives or negatives. Also, assess the interventions that yield the greatest results and adjust alert thresholds and workflows accordingly.

Predictive maintenance helps organizations increase their OEE score, bringing them closer to achieving world-class OEE.

Wrapping Up

Predictive maintenance helps organizations increase their OEE score, bringing them closer to achieving world-class efficiency. It’s powered by IIoT, real-time analytics, and ML technologies. As a reactive approach, predictive maintenance has many benefits compared to traditional reactive and preventive maintenance. It enables real-time machine health monitoring and helps forecast issues before they occur.

Predictive maintenance is enabled by time series databases like InfluxDB 3. InfluxDB 3 efficiently handles high-velocity machine data, provides scalable and affordable storage, integrates with AI/ML for predictive insights, and delivers fast query performance for rapid decision-making.

Start leveraging predictive maintenance today with InfluxDB 3 to maximize OEE, minimize losses, and reduce downtime. Try our different plans, InfluxDB Enterprise, InfluxDB Cloud Dedicated, or InfluxDB Clustered, to see how we can address your predictive maintenance needs.

Sponsored by InfluxData