Why You Need a Time Series Database for Predictive Maintenance
In the industrial sector, unplanned downtime can lead to significant financial losses. Predictive maintenance offers a solution to this problem by identifying potential issues before they cause disruptions.
One crucial component of an effective predictive maintenance system is a time series database. These specialized databases, designed for use cases involving time series data, are ideal for storing large volumes of sensor data generated by industrial equipment.
In this article, we’ll delve into the role of time series databases in predictive maintenance, exploring how they facilitate early problem detection, enhance decision-making, and ultimately contribute to increased operational efficiency and cost savings.
What is predictive maintenance?
Predictive maintenance is a forward-thinking approach that focuses on predicting when organizations should perform machine maintenance. This strategy relies on the real-time data collected from machinery to anticipate potential failures before they occur, allowing users to schedule maintenance at the most opportune time.
Unlike reactive maintenance, which deals with repairs after a failure occurs, or preventive maintenance, which follows a predetermined schedule regardless of actual equipment condition, predictive maintenance tailors maintenance tasks based on the actual condition of the equipment. This approach leads to significant benefits, such as enhanced cost efficiency by reducing unnecessary maintenance and parts replacement and a substantial reduction in downtime by addressing issues before they lead to machine failure.
In various industries, predictive maintenance finds unique applications. Here are a few examples:
• Manufacturing – Predictive maintenance can be applied to the factory itself as well as the products created by the factory.
• Energy – Utility companies with windmills often use predictive maintenance to make repairs more efficient in remote locations where technicians may be required to travel long distances.
• Aerospace – Predictive maintenance for aircraft engines helps to reduce critical failures and unplanned grounding of commercial aircraft that could impact scheduled flights.
How does a time series database help with predictive maintenance?
Time series databases are specifically designed to store and analyze time-stamped data, making them a good choice for predictive maintenance as a data storage solution. These databases are capable of handling large volumes of data, ensuring that all relevant information is captured and stored for historical analysis. Time series databases also provide fast query performance, allowing for real-time analysis and decision-making.
One of the key advantages of time series databases in predictive maintenance is their ability to store and analyze data over long periods with high-granularity for more precision. This enables the identification of long-term trends and patterns in equipment behavior, which can indicate potential problems. By leveraging historical data, time series databases can help maintenance teams predict when equipment is likely to fail, allowing them to take proactive measures and prevent unplanned downtime.
Time series databases typically offer built-in data compression and aggregation techniques, which can significantly reduce the storage space required and improve query performance. This is particularly important in predictive maintenance scenarios where large volumes of data are continuously generated and need to be stored and analyzed efficiently.
Time series databases can easily integrate with other software and systems used for predictive maintenance, such as data visualization tools and machine learning models. Flexibility in architecting your predictive maintenance system allows you to choose the best tool for each task.
Time series databases vs. data historians
A question that often arises when discussing how to build out predictive maintenance systems for IoT use cases is how time series databases differ from data historians. Both can function in predictive maintenance but with different strengths and weaknesses.
Data historians are used in industrial settings to monitor and control equipment. Data historians typically act as a complete platform with built-in visualization, integration, data analysis, and other tools. Data historians can be easier to implement, but the downside is less flexibility and vendor lock-in.
Time series databases are the more general-purpose option, used in applications that require real-time data processing for time series data like monitoring and financial trading. Combining a time series database with other best-in-class tools allows you to create tailored solutions for specific predictive maintenance use cases.
Deciding whether to use a time series database or a data historian comes down to your team’s skill set and involves most of the tradeoffs you see in “build vs buy” situations.
Real-world examples using time series databases for predictive maintenance
Here are a few examples of companies that use time series databases in the real world to power their predictive maintenance solutions.
MAJiK Systems are the creators of a factory and manufacturing monitoring platform. MAJiK provides a number of tools that allow companies to improve their OEE scores via real-time analytics. Some examples include 45% reduced downtime due to predictive maintenance and 10% reduction in scrap waste.
LBBC are leading designers of autoclave technology used by aerospace customers building high-performance engine turbine blades. By using a time series database, they optimized their processes using anomaly detection and enabled predictive maintenance by finding hidden patterns in their data.
Olympus Controls is a company that specializes in building robotics for manufacturing plants and factories. They use data like vibration and temperature from robotic arms and other devices to enable predictive maintenance for their customers.
The integration of time series databases into predictive maintenance strategies represents a major advancement in industrial operations. This combination enhances the efficiency and effectiveness of maintenance tasks while driving significant cost savings and operational improvements. Businesses looking to implement or upgrade their predictive maintenance systems should include exploring time series databases as part of the process. As we continue to embrace the digital transformation of industrial operations, staying informed and adapting to these technological advancements will be key in maintaining a competitive edge.
About the author
This article was written by Charles Mahler is a Technical Writer at InfluxData where he creates content to help educate users on the InfluxData and time series data ecosystem. Charles’ background includes working in digital marketing and full-stack software development.