Time Series Data
What is time-series data?
Time series data is a type of data that’s collected about a single subject over time. It’s usually used in contrast to cross-sectional data, which is information about multiple subjects that’s recorded at a single point in time.
Process plants generate enormous amounts of time series data because that’s what’s recorded by every IIoT device and equipment sensor. Time series analysis unlocks time series data, using its own tools and methods, and uses it to present a detailed and rich story about the life of the plant.
Today, time series data is used by almost every manufacturing plant, but for a long time, the potential of time series data went untapped. Time series data is only valuable when it’s collated and timestamped, which was difficult before IIoT made it possible to align every datapoint with its time of origin.
Why is time-series data important for process manufacturing plants?
On a basic level, time series analysis reveals trends like seasonality, recurrences, changes to regular patterns, and explosive data variations. Time series data allows process engineers to analyze drift, for example, to see the severity of the drift, when it began, and how fast it is changing.
But when Machine Learning modeling is applied to time series data, it’s possible to make real-time decisions, forecast demand, perform root cause analysis, and more. Time series data powers autonomous decisions that characterize a smart factory and underpins complex ML applications. It’s the basis of most advanced analytics and AI solutions, like predictive analytics, predictive maintenance, and predictive monitoring.
By using ML to analyze time-series data, process plants can:
- Increase efficiency and agility
- Spot early signs of failure, process deviations, and bottlenecks
- Reveal emerging opportunities
- Improve maintenance scheduling
- Refine plant operations
- Reduce downtime
- Increase consistent product quality
How can process plants apply time-series data?
Unite your data sources
Time series data is more valuable when you can correlate it with cross-sectional data as well as time-series data from other IIoT sources. Establish a source-agnostic data repository that can store all your data sets and preprocess them to remove data silos and enable your time series analysis tools to reach all the information they need.
Train your models
Time series analysis uses ML modeling to spot patterns and extrapolate trends. It can produce insights in real-time, but first, you need to allow a training period for the ML models to be trained on your historical data and learn how to recognize serious anomalies among all the noise of a normal busy plant.
Make insights accessible to all
Once you’ve set up your time series data collection system and established ML analysis models, you need to make sure that the insights and predictions you produce are accessible for all your users. Choose a solution with an intuitive, user-friendly interface that doesn’t require specific data science skills.
How do process plants benefit from time-series data?
Time-series data offers process plants the opportunity to gain a better understanding of everything that’s going on in the plant. With the help of ML time series analysis tools, a process plant can become more versatile, agile, and efficient to help ensure it remains profitable in a competitive market.