Self-service analytics for non-data-scientists
Why predicting and averting process and equipment problems requires a new generation of analytics tools.
Though plant operations typically generate mind-boggling quantities of data—both structured and unstructured—plant engineers and operations personnel can only leverage a small percentage to make better process decisions. This is because most process data from plant systems is stored in online and off-line process historian archives. These archives are “write”-optimized and not “read/analytics”-optimized, so finding related historical events and building process context can be a time-consuming and laborious task. Process historians are useful for storing process data and connecting to real-time systems, but lack of a mechanism for search and the ability to annotate effectively.
To solve this, many engineers resort to using Excel spreadsheets, but this is time-consuming and limited in functions. Other tools used to visualize and interpret process data are typically trending applications, reports and dashboards. These have been helpful, but not particularly good at predicting outcomes.
Analytics are essential
Improving process performance and overall efficiency requires operational intelligence and an understanding of data. One of the basic elements is that process engineers and other stakeholders must be able to search time-series data over a specific timeline, and visualize all related plant events quickly and efficiently. This includes the time-series data generated by the process control, lab and other plant systems, as well as the usual annotations and observations made by operators and engineers.
Process engineers and operators need to accurately predict process performance or the outcome of a batch process, while eliminating false-positive diagnoses. Traditional, backward-looking, “describe and discover” analytics solutions are little help here. Accurately predicting process events that will likely happen in a facility requires accurate process historian or time-series search tools and the ability to apply meaning to patterns identified in process data.
By combining both the search capabilities on structured time-series process data, and data annotated by operators and other SMEs, users can understand more precisely what’s occurring and predict what likely will occur in their continuous and batch processes.
A big black box
Process analytics solutions in one form or another have existed in the industrial software market for some time. These largely historian-based software tools often require much interpretation and manipulation by the user. Predictive analytics, a relatively new dimension to analytics tools, can provide users with valuable insights about what will happen in the future based on historical data, both structured and unstructured. However, many of these advanced tools tend to be perceived as engineering-intensive “black boxes” targeted toward power users who can make needed interpretations. For a lot of operational and asset-related issues, this approach is not economically practical (negative ROI). That’s the reason a lot of vendors are targeting only that 1% of critical assets.
Other predictive analytics tools start by using a more enterprise-based approach, and require more sophisticated, distributed computing platforms such as Hadoop or Cloudera. These are powerful and useful for many analytics applications, but represent a more complex approach to managing plant and enterprise data. Companies that use this enterprise data management approach often employ specialized data scientists to help organize and cleanse data.
“There’s an immediate need to search time-series data and analyze them in context with the annotations made by engineers and operators to make faster, higher-quality process decisions. If users want to predict process degradation or an asset or equipment failure, they need to look beyond time-series and historian data tools, and then search, learn by experimentation, and detect patterns in the vast pool of data that already exists in their plant.”
The next-generation: self-service
There are new solution suppliers that are taking a different approach to providing industrial process data analytics, and leveraging multidimensional search capabilities for stakeholders. The approach combines the necessary elements to visualize a process historian’s time-series data, overlay similar matched historical patterns, and enrich with data captured by engineers and operators. A form of “process fingerprinting” then provides operations and engineering with greater process insights to optimize the process and/or predict unfavorable process conditions. Furthermore, unlike traditional approaches, performing this analysis doesn’t require the skill set of a data scientist.
Key elements of the approach include:
- A system that brings together deep knowledge of process operations and data analytics techniques to minimize the need for specialized data scientists or complex, engineering-intensive data modeling, which can turn human intelligence into machine intelligence to gain value from operational data already collected.
- A model-free, predictive, process-analytics (discovery, diagnostic and predictive) tool that complements and augments, rather than replaces, existing historian data architectures.
- A system that supports cost-efficient virtualized deployment and is “plug-and-play” within the available infrastructure, yet has the ability to evolve into a fully scalable component of corporate Big Data initiatives and environments.
Multidimensional search solutions
The technology playing field for manufacturers and other industrial organizations has changed. Owner operators now have different tools for improving plant availability and asset effectiveness.
There are now methods of connecting to existing historian databases, and using a column store database layer for their index creates a clever front end that allows multi-dimensional, search-based analytics queries. Using pattern recognition and machine learning algorithms can allow users to search process trends for specific events or detect process anomalies, which can make the systems that use this approach distinct from traditional historian desktop tools.
Next-generation solutions come as on-premise, packaged, virtual server deployments, easily integrated with the local copy of the plant historian database archives, and able to evolve over time toward a scalable architecture and blend in with available, enterprise-distributed computing platforms. The newer approach uses “pattern search-based discovery and predictive-style process analytics” targeting the average user. It’s relatively easy to deploy and use, providing the potential for organizations to gain immediate value without a big data or modeling solution by placing the power of the analytics insights in the hands of the users.
“The new platforms are built to make operator shift logs searchable in the context of historian data and process information. At a time when the process industries may face as much as a 30% decline in the skilled workforce through retiring workers, knowledge capture is a key imperative for many industrial organizations.”
These next-generation systems also work well with leading process historian suppliers, including OSIsoft, AspenTech and Yokogawa. The technology forms the critical base layer of the new systems’ technology stack because it uses existing historian databases, and creates a data layer that performs a column store to index the time-series data. Typically, it’s designed to be simple to install, connect and get up and running with the use of a virtual machine (VM), without impacting the existing historian infrastructure.
Please contact us for further information or to discuss your specific needs.
More information?
ARC Advisory Group (ARC), the global technology research firm, has a unique perspective on emerging trends impacting industry today. In this white paper, ARC analyzes the next generation of industrial analytics solutions and examines how a self-service approach can help companies to optimize their production processes.
