We all understand the internet has transformed the world. However, we often forget the tremendous difficulties of navigating the web in the early days of the digital age. Prior to the introduction of directories and search engines like Alta Vista, Yahoo and Google, locating specific information from the vast amounts of data on the web was time consuming and frustrating.
This comparison also applies to companies trying to leverage the huge amounts of data collected by their various systems. Companies have been very good at collecting data, but until now have lacked affordable and effective tools needed to search and interpret it for actionable information.
The Challenge of Big Data
For many years, various automation software vendors tried to simplify data retrieval and presentation. When historians were introduced in the 1980s their function was storing process data and generating reports; they were not designed for easy data mining or visualization needed for predictive analytics.
More recently, leading companies have wanted to access captured time series data from their historians to optimize operations. Unfortunately, typically only five percent of historian data was being transformed into actionable information. It was simply too hard, too expensive and too time consuming for their engineers to use historian data for process improvement.
Industrial Internet of Things (IIoT)
The industrial sector is currently undergoing another revolutionary transformation: the Industrial Internet of Things (IIOT) or Industry 4.0. IIoT proposes a new way to improve business intelligence and consequently boost operational efficiency using data-driven analytics.
Along with increased functionality, IIoT promises to generate even more data. It will introduce new types of data and formats, increase data volume and ideally transform operational decision making from reactive to proactive.
Companies wanting to benefit from big data will require a new approach to accessing information and analytics. Many traditional architectures and processes will not be able to deliver timely insights, resulting in missed opportunities. Enormous amounts of data will quickly overwhelm anyone who cannot easily locate specific information; therefore, the ability to manage and interpret information will be the key to success.
Early attempts to transform raw data into useful actionable information relied on data modeling. The problem is data modeling requires complex IT projects and data scientists to build and maintain models. Thus, the projects are time consuming and expensive, which limits the number of searches performed.
The concept of Google for Industry, an approach for easily accessing specific information from historians, was created from engineers’ experiences in the process industries. They had worked with nearly all types of analytics models and identified their limitations for scaling-up beyond pilot projects.
Google For Industry is Born
One of the first challenges was existing solutions were based on data modeling. In addition to being time-consuming and expensive, data modeling projects were sensitive to change, with no flexibility.
All modeling technologies must go through the same steps:
- Data preparation
- Data modeling
- Data validation
- Bringing the model live.
Therefore, each time a model is changed, it must go through the same cycle. Moreover, data modeling is not designed for dynamic processes because it is often based on assumptions about stationarity and data distribution that do not hold in a real process with variables.
Data Modeling Difficulties
Requires significant engineering
Data cleaning, filtering, modeling, validating, iterating on
Sensitive to change
Users needed continual training
Requires data scientist
Plants had to hire additional workers, or engineers spent too much time trying to be data scientists
Not plug and play
Installation and deployment required significant investments in time and money
Black Box Engineering
User cannot see how results are determined
An initial project to find an easier way to access and interpret historian data (see sidebar) resulted in a user-friendly, highly sophisticated search for process data in which advanced pattern recognition algorithms find either similar or deviating behavior. Users simply choose their reference period over one or more tags then the system finds similar behavior throughout the entire data history.
It didn’t take long for the Google for Industry concept to grow. People needed to search far beyond only similar patterns in the past. Thus, the search capabilities were extended to basically all relevant things sought in daily searches: behavioral patterns, slopes, operator actions, certain switch patterns, conditional or Boolean conditions based on tags, drift, oscillation of a certain frequency, anomalies, event frames, context, and more.
How it Works
The Google for Industry approach started with search, but its vision goes much further. Software created for high performance discovery analytics engine for process measurement data delivers the power and ease of Internet search engines to industrial applications. Through an intuitive web-based trend, users can start searching for trends with pattern recognition and machine learning technologies.
Using value or digital-step searches for filtering in and out of times or finding something that looks similar is just the beginning. Process engineers can also search for specific operating regimes, process drifts, operator actions, process instabilities or oscillations.
Root cause analysis is very important for keeping operations running smoothly. The causal and influence factor search algorithms show users the underlying reasons behind process anomalies or a deviating batch.
Comparing behavior between an anomaly and a normal operating period had been a painful process. Now, new software provides a faster, more accurate analysis of a continuous production process by employing advanced search and diagnostic capabilities to compare a large number of transitions focusing on both equalities and differences.
Furthermore, live displays show process values as they evolve while the software can also predict the most probable evolution of these values in the future based on matching these on historical values.
From Reactive to Proactive Decision Making
To shift from reactive to proactive decisions requires the ability capture information from multiple users – process engineers, operators, maintenance staff, etc. – in one single environment connected to the relevant process data provides context.
The next step is using search features to create a golden profile by finding the best transitions or batches from multiple historical transitions to enable predictive monitoring. Users can take a live view of a process and apply it over the golden profile to verify that recent changes are behaving as intended. Users can then proactively adjust settings for optimal performance or test to see if changes will produce the right results before they are implemented.
Furthermore, the software provides sophisticated alarm capabilities. Instead of merely sending an alert that a problem is occurring, the software configures meaningful alarms to prevent problems.
For example, a process engineer can review the operators’ annotations of a past problem. He performs a search to find four comparable anomalies in the past. Based on an overlay of the resulting periods, he can easily determine alarm limits to prevent a similar occurrence happening again.
As we have seen, highly advanced software capabilities simplify the ability to make proactive decisions by providing user-friendly search with predictive analytic capabilities provided by powerful algorithms.
Thus, this new approach to accessing and analyzing data will enable process industry companies to unlock the valuable information in their historians to maximize efficiencies and reduce downtime.
Bert Baeck is the CEO of TrendMiner.