Predictive Maintenance in IIoT: Leveraging Time Series Data for Equipment Longevity

Predictive Maintenance in IIoT: Leveraging Time Series Data for Equipment Longevity | SPONSORED

The industrial Internet of Things (IIoT) helps bind machines, sensors, and systems together. They form a network of intelligent devices working together to generate and share data. This approach to managing devices has revolutionized how industries manage, monitor, and maintain their equipment. This connected network provides visibility into equipment performance, process throughput, and overall productivity. 

Predictive maintenance uses real-time data and analytics to determine the condition of equipment, allowing maintenance teams to make proactive adjustments. This helps increase equipment production, minimize downtime, and prolong the life of the equipment.

Time series data is the routine collection of data from sensor readings such as equipment temperature, pressure, and vibration. This data helps teams find patterns that indicate anomalies. One example of a leading time series database (TSDB) is InfluxDB. InfluxDB provides capabilities for collecting, storing, and analyzing huge amounts of generated timestamped data. 

In this post, we’ll learn how predictive maintenance uses time series data in IIoT environments. We’ll also discuss how predictive maintenance helps industrial operations and how InfluxDB solutions can support this. 

Benefits of predictive maintenance

Predictive maintenance provides multiple advantages in an IIoT environment. Let’s discuss some of them. 

Reduced Downtime

The biggest advantage of predictive maintenance is its ability to reduce unscheduled downtime. By putting sensors on the equipment and monitoring them, companies can use time series analysis of the data collected by the sensors. This analysis can give you an indication of whether anything may be wrong with the equipment. This warning then allows you to determine beforehand if there will be a problem, and you can make sure to schedule the maintenance in the planned window. 

Cost Savings

Predictive maintenance saves costs by tracking the condition of your equipment and optimizing your hardware utilization. Routine or defined maintenance is costly to a business when it occurs. By using real-time data from sensors, you can determine which part really needs maintenance, and replace only that part. 

Extended Equipment Lifespan

With predictive maintenance, you can catch problems when they’re small and complete maintenance quickly. This helps extend the life of the sub-part where the problem was caught. 

Increased Operational Efficiency

Predictive maintenance increases operational efficiency in several ways. The process prevents any unexpected breakdowns and properly schedules maintenance, keeping the production lines working more smoothly. It also ensures that enough maintenance is planned so that no unusual installations or measures are required. 

Key Components of Predictive Maintenance in IIoT

Multiple components are present in predictive maintenance that help you decide the maintenance strategy for your equipment. Let’s discuss these key components and how they work together to enable predictive maintenance. 

Data Collection from Sensors

Predictive maintenance systems are entirely dependent on data from sensors embedded in the equipment. These sensors measure vibration, temperature, pressure, humidity, and power consumption, and their accuracy depends on the frequency of data collection. Data is captured multiple times a day so small changes are measured, helping the maintenance team recognize threats to the systems at early stages. 

Storing and Managing Data in Time Series Databases (TSDB)

Once the data from sensors is collected, it’s accumulated to form the time series data, which is managed in a TSDB. Since data collection occurs frequently, the volume of data can become unruly. TSDBs like InfluxDB manage this timestamped data effortlessly. 

InfluxDB offers data compression techniques that reduce storage costs while maintaining the integrity of the original data. TSDB also provides efficient querying capabilities optimized for time-based queries. This feature helps maintenance teams analyze long-term trends and current equipment performance compared to earlier baselines. 

Analyzing Data for Predictive Maintenance

After collection, data requires analysis. TSDBs like InfluxDB can be integrated with machine learning models to analyze time series data for insights and pattern anomalies. These analytics systems can find changes from normal operating conditions, predict future performance based on historical trends, and detect small changes. 

Analysis of data can help in setting up thresholds and alerts based on historical data and expert knowledge. These thresholds can trigger notifications when certain conditions are met, prompting maintenance actions before failures occur. 

Maintenance Scheduling and Decision Making

The final stage of data analysis is scheduling maintenance and making decisions. Predictive analytics allow maintenance teams to schedule maintenance during planned windows, minimizing the impact on production. 

Decision-making helps to determine whether components should be repaired or replaced. By analyzing performance trends and failure probabilities, maintenance teams can make informed decisions about the most cost-effective course of action, balancing the costs of replacement against the risks of continued operation. 

The role of time series data in predictive maintenance

Predictive maintenance has multiple components that enable you to decide the maintenance strategy for your equipment. In this section, we’ll discuss how these components work together to enable predictive maintenance. 

Why Time Series Data Is Essential for Predictive Maintenance

Time series data plays a central role in predictive maintenance by offering time-based monitoring of equipment health. Businesses can monitor their equipment’s performance to reveal trends and patterns. This way, you can detect performance anomalies that might continue to develop and eventually break down a piece of equipment.

Time series data can provide real-time insights that help maintenance teams take action as soon as an anomaly is discovered. Any deviation from normal operating conditions can trigger an alert, potentially saving businesses from major breakdowns in the future. 

How Time Series Data Enhances Equipment Longevity

Time series data is vital to the functionality of equipment lifespans because it allows us to quickly understand wear patterns, degradation of performance, and the beginning of a failure before it progresses. Specifically, recording a machine’s operation creates a highly detailed pattern that may show peculiarities if compared to a historical dataset for the same model. Addressing these concerns earlier prevents a higher degree of damage from happening, thus increasing the lifespan of the particular machine. 

In addition, time series data helps develop predictive models that predict the failure point for a cumulating error. Addressing these concerns at exactly the right time requires precision and calculation. This allows businesses to replace only the absolutely necessary parts and keep their machines in the best condition, ultimately reducing replacement costs and improving overall operational efficiency. 

InfluxDB: The Ideal Time Series Database for Predictive Maintenance

Predictive maintenance relies on time series data sources and, therefore, requires a reliable, highly performing, and scalable database solution. This is where InfluxDB comes in. Its products are specifically designed to meet the demands of IIoT. InfluxDB is the solution for rapid input, storage, and querying of time series. Millions of data points per second can be written into the system and indexed in real-time. 

InfluxDB combines high performance with high-speed data compression algorithms, enabling long-term (or even infinite) data storage, without significantly increasing cost. 

InfluxDB offers different products that can used for industrial IoT applications as per their needs: 

• InfluxDB Cloud Serverless: a fully managed, auto-scaling solution
• InfluxDB Cloud Dedicated: a single-tenant, fully managed offering for enhanced performance and security
• InfluxDB Clustered: a self-managed, horizontally scalable solution for maximum control

Why InfluxDB for Predictive Maintenance

InfluxDB’s features make it well-suited for predictive maintenance applications in IIoT environments. Some of these features include: 

• Real-time data processing: Timely anomaly detection and prevention are important in predictive maintenance. InfluxDB helps process massive volumes of IIoT sensor data with low-latency ingestion and querying. InfluxDB processes real-time data from machines immediately to inform maintenance teams of potential issues and suggest proper measures.
• Scalability: As businesses develop, the number of connected machines and, hence, data is expected to grow. InfluxDB’s architecture allows it to be easily scaled as the business grows. This feature is particularly advantageous in industrial environments, as the data growth is synchronized with the growth of operations.

Integration: InfluxDB easily integrates AI/ML tools and analytics platforms. Integration is essential for implementing advanced predictive algorithms and optimizing maintenance schedules.

Wrapping up

Time series data is the foundation of efficient predictive maintenance in IIoT. The detailed information on equipment that time series data provides allows businesses to track even minor changes, estimate and predict failures, and adjust maintenance schedules for them. As a result, downtime and cost significantly decrease, and equipment life is prolonged. 

InfluxDB is a powerful solution for time series data management. It can be scaled and used reliably in predictive maintenance. Owing to its time series data management, InfluxDB may enhance the performance of predictive maintenance strategies. With its integration with AI/ML applications for advanced predictive maintenance, InfluxDB time series data management provides more accurate and valuable results. 

Any industrial business should consider implementing InfluxDB for time series data management in predictive maintenance strategies. Using InfluxDB, businesses have a real opportunity to benefit from using time series data to better predict the future of their equipment.

Sponsored by InfluxData

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