Unlocking the Power of IIoT with Time Series Databases

By Jason Myers / Developer
Feb 09, 2024

This article was originally published on IIoT World and is reprinted here with permission.

In the rapidly evolving world of Industrial Internet of Things (IIoT), organizations face numerous challenges when it comes to managing and analyzing the vast amounts of data generated by their industrial processes.

Data generated by instrumented industrial equipment is consistent, predictable, and inherently time-stamped. This allows organizations to monitor how their equipment, machines, and systems are functioning, remotely and in real-time. The prevalence of automation and machine learning helps shoulder industrial grunt work, heightening productivity, cutting unnecessary downtime, and leaving developers and engineers free to innovate.

Traditional databases and data historians struggle to handle the unique characteristics of time series data, hindering the ability to gain valuable insights and optimize operations in the modern industrial setting. This is where adopting a time series database can make a significant impact. Let’s explore a real-world example to understand the challenges faced by IIoT organizations and how a time series database can resolve them.

Example: optimizing energy consumption in a manufacturing plant

Challenge

Imagine a manufacturing plant that aims to optimize its energy consumption to reduce costs and minimize environmental impact. The plant relies on various sensors and devices to monitor energy usage, such as power meters, temperature sensors, and pressure sensors. These sensors generate continuous streams of time-stamped data, providing valuable insights into energy consumption patterns. However, the plant faces several challenges in effectively utilizing this data:

• Data Volume and Velocity: The sheer volume and velocity of data generated by sensors make it difficult to store, process, and analyze in real-time using traditional databases. The plant needs a solution that can handle high-frequency data streams and provide real-time insights. This solution also needs the ability to integrate with legacy tools and platforms, as well as next-generation cloud systems to maximize value.
• Data Granularity: Energy consumption patterns can vary significantly based on different factors, such as time of day, production schedules, and equipment usage. Different sensors may generate data at different intervals, sometimes on the same machine. The plant needs to capture and analyze data at a granular level to identify patterns and optimize energy usage accordingly.
• Data Retention: Historical data is crucial for trend analysis, predictive maintenance, and compliance purposes. The plant requires a database that can efficiently store and retain large volumes of highly granular time series data for extended periods without sacrificing performance.

Impact

Without a suitable time series database, the manufacturing plant faces several limitations:

• Inability to Optimize Energy Consumption: The plant cannot effectively identify energy usage patterns or detect anomalies in real-time, leading to missed opportunities for optimization and cost savings.
• Limited Predictive Maintenance: Without access to historical data, the plant cannot accurately predict equipment failures or schedule maintenance activities, resulting in unplanned downtime and increased maintenance costs. This is a key area for interoperability as well. High-volume time series data is a natural source for training and maintaining machine learning models.
• Lack of Real-Time Insights: Traditional databases struggle to provide real-time analytics, preventing the plant from making timely decisions to optimize energy consumption and respond to critical events.

Solution: adopting a time series database

Time series databases like InfluxDB are specifically designed to handle time-stamped data, offering a comprehensive solution to the challenges faced by the manufacturing plant. Opting for an open-source-based solution provides organizations with tons of flexibility. Once viewed as a “budget” option, open source tools are now standard, if not preferred, due to their extensive interoperability. An extensible time series database offers solutions for data modeling, tagging, and ingestion in IIoT applications and aligns with the principles of interoperability, information transparency, and decentralized decision-making. Here’s how a time series database like InfluxDB resolves the aforementioned issues:

• Efficient Data Storage and Processing: A time series database can handle the high volume and velocity of time series data, ensuring efficient storage, retrieval, and processing. InfluxDB, for example, enables real-time analytics, allowing the plant to monitor energy consumption patterns and detect anomalies promptly.
• Granular Analysis: With a time series database, the plant can capture and analyze data at a granular level (even at nanosecond precision), enabling detailed insights into energy consumption patterns. This lets the plant identify inefficiencies, optimize energy usage, and make data-driven decisions.
• Long-Term Data Retention: Time series databases are designed to handle large volumes of data over extended periods. Keeping all that high-fidelity data long-term can be very expensive and lacks attention to data compression and storage media. For example, a columnar database can compress data more efficiently than a row-based database. Saving data to low-cost object storage instead of in-memory or on SSDs means that organizations can store more data in less space and for less money. The ability to keep this data long-term means the plant can retain historical data for trend analysis, predictive maintenance, and compliance purposes, enabling better decision-making and improved operational efficiency.

Final thoughts

Adopting a time series database is crucial for IIoT organizations looking to unlock the full potential of their data. Efficiently storing, processing, and analyzing time series data allows organizations to gain valuable insights, optimize operations, and make data-driven decisions. The example of optimizing energy consumption in a manufacturing plant highlights the challenges IIoT organizations face and how a time series database can resolve them. Whether organizations replace legacy data historians, augment them with newer technology, or somewhere in between, embracing a purpose-built time series database like InfluxDB empowers organizations to thrive in the era of Industry 4.0 and drive innovation in their industrial processes.