How to empower industrial applications with machine learning?

Equipment failures pose serious problems to the industrial sector, resulting in production losses and unplanned downtime. This situation represents a serious challenge to process manufacturers worldwide, causing losses that can run into billions of dollars annually. For example, if a key production equipment suddenly fails, it may cause the entire production line to shut down for several hours, thus affecting the operation of the entire supply chain.

Fortunately, modern machine learning (ML) offers a breakthrough solution. By analyzing large amounts of sensor data, ML algorithms can predict failures and backlogs before they occur, enabling proactive repairs and significantly reducing downtime. But that’s not all, ML also reveals hidden patterns in production data, optimizing processes, reducing waste and improving overall efficiency.

Before organizations can realize the full potential of machine learning, they must first start strengthening the fundamental elements of team collaboration. To build accurate and impactful models, data scientists and domain experts need to develop close collaborations and a deep understanding of the complexities of industrial equipment. Their collaboration will translate expertise on the shop floor into the language of data, driving the successful application of machine learning solutions.

Overcoming the shortcomings of traditional industrial data

Using ML insights to improve operational efficiency does not happen overnight. The first challenge is making sense of raw industrial data.

In its native format, industrial data is massive, diverse, and often filled with erroneous or irrelevant information, such as outage logs. Without guidance, data scientists often waste valuable time and resources sifting through irrelevant complexity, wasting valuable time and often producing misleading models. This is why domain experts, including process engineers and operators, are critical in preparing data for accurate models. Their extensive process knowledge helps determine the correct data and relevant time periods.

However, identifying the right data is only the first step. Raw industrial data is often messy and requires context to understand. Imagine having a model where temperature readings during maintenance are mixed with temperature readings during operations: this would throw a predictive model into chaos! Inserting data into a model without a clue can wreak havoc, suggesting that when performing analysis The importance of cleaning and contextualizing data beforehand. Process experts can help identify such considerations, reduce algorithm errors, ensure consistency, and identify the specific operating conditions most important to model success.

After the data is cleaned, there is still a lot of work to be done to get it ready for ML. Feature engineering bridges this gap, requiring ongoing collaboration between data scientists and process experts to transform raw readings into contextual insights that directly address the problem at hand. These information insights, or "signatures," include statistical summaries, frequency patterns, and other clever combinations of sensor data that help ML algorithms discover hidden patterns, improve model accuracy, and assist with complex operational decisions.

Deploying ML models in industrial settings requires more than just accuracy. To truly generate value, models must be easily transferable to operators for use in the production process. This means the interface must be easy to read and present forecasts, alerts and real-time data clearly and concisely. Additionally, where feasible, including explanations in the operational interface builds trust and understanding among end users.

Industrial processes change over time, so successfully deploying machine learning requires models to be regularly retrained with new data to ensure their accuracy. This requires close collaboration between data scientists and operations teams to monitor performance and continually iterate on models.

Advanced Analytics Enhances Industrial Machine Learning Initiatives

The many steps of building and implementing ML models in operational workflows are not easy, but modern advanced analytics solutions are simplifying the process for ML integration Provide overall solutions for industrial processes.

These solutions break through the common industrial data clutter by connecting multiple data sources in real time. In addition to aggregation, these software tools can automate data cleaning, eliminating a lot of manual data processing and reconciliation (Figure 1).

Figure 1: Seeq automates data cleaning using a series of built-in smoothing methods to provide a contextualized picture of plant performance. For example, two bad temperature readings are automatically removed from the purified process variables, which are used for modeling and creating process insights.

This adaptability is critical when processes change, as it keeps ML models up to date and providing relevant information to reflect current operating conditions. For example, in conveyor belt failure scenarios, advanced analytics solutions enable engineers to quickly identify anomalies, handle inconsistencies and extract meaningful information immediately. This high-quality data can then inform troubleshooting steps, provide actionable ML insights, and increase confidence in operational decisions.

Feature engineering is critical to the success of machine learning in industrial settings, but it requires collaboration. Advanced analytics solutions help facilitate this needed synergy through clearly curated user profiles built for different expert roles, along with the tools needed to seamlessly share findings across operational teams (Figure 2).

Figure 2: Seeq implements ML by making it easy to build automated reports and dashboards where engineers and data scientists can share their analysis results with line of business and operations teams to drive daily value.

For example, Seeq’s Data Lab enables data scientists to easily deploy models for direct use by engineering and operations teams, who can provide feedback to help refine the models. Predictions and alerts then flow to Workbench, Organizer, and external visualization tools, which are typically accessible to administrative users. Advanced analytics solutions bridge these historically separate departments, transforming models into powerful tools for tighter process control, operational optimization and smarter decision-making across the organization.

Controlling Compressor Failures with Predictive Analytics

Real results show that advanced analytics solutions can effectively reduce costly downtime events. For example, a large chemical manufacturer plagued by unexpected failures of critical compressors used the Seeq solution to identify subtle deviations in compressors from one operating cycle to another. With losses estimated at $1 million per incident, finding a way to predict and prevent these failures quickly became a priority.

The company began collecting large amounts of process data, but it was so large and complex, with more than 170 variables, that it was difficult to discern true patterns from the noise. Traditional analysis methods cannot identify the combination of factors that may have caused the failure.

The manufacturer turned to Seeq, leveraging the software’s built-in ML tools to enable its domain experts to solve model development problems without relying entirely on data scientists. The solution’s user-friendly interface puts the power of ML directly into the hands of process engineers with comprehensive compressor expertise, helping to close the knowledge gap between SMBs and data scientists that is more difficult to achieve with traditional analytics. For serious. This helps ensure that predictive models incorporate the correct domain understanding and evolution.

By leveraging purpose-built capabilities in advanced analytics solutions, the company transforms model results into near real-time operational insights. The models focus on subtle deviations in compressor parameters that indicate problems, and visual dashboards help alert operations and engineering teams early on to take preventive action to avoid costly failures. This predictive approach enables teams to transform reactive maintenance into a proactive strategy.

By fixing problems before they fail, the company significantly reduces costly downtime events. Advanced analytics solutions not only provide a technical backbone but also provide new data fluidity, giving engineers greater control over equipment health.

Solve frozen meter issues and optimize gas delivery

Frozen meters threaten the profitability of oil and gas suppliers, leading to measurement errors and costly product waste. The scale of the problem is amplified by one operator's vast network, which spans 32,000 miles of pipeline and handles 7.4 billion cubic feet of natural gas per day. Cluttered data and reliance on rules-based approaches to identify freezing events proved to be time-consuming and unreliable, and maintaining rules consumed valuable resources in addition to filtering through many false positives and missed detections.

The company needed a new way to streamline cleaning and access its vast amounts of meter data. Domain experts use software tools to improve data quality and annotate past frozen events, while data scientists work with engineers to develop accurate models, move beyond rigid rules and embrace ML.

In advanced analytics solutions, operators establish a fully automated workflow that includes data preprocessing, model configuration, and automated retraining to maintain model accuracy as operating conditions change. Model predictions feed directly into visual dashboards and populated reports, providing stakeholders with real-time insights into potential freezing issues.

This streamlined workflow can proactively intervene to mitigate freezing issues, even if accuracy improves slightly in some places, resulting in millions of dollars in annual savings from reduced product giveaways. In addition to improving accuracy, the solution facilitates data-driven collaboration, which is critical to continuously improving operational efficiency.

This work brings three important takeaways for vendors:

• Scalability: Advanced analytics solutions can handle a company’s massive data sets, which are large-scale assets Key advantages of management.
• ML as an efficiency multiplier: Automated detection tasks enable engineers to focus on higher value problems.
• From insights to profitability: Advanced analytics solutions simplify the process from prediction to cost savings, an important sign of effective ML deployment.

Effective introduction of machine learning in industrial environments

It is undeniable that machine learning is changing the manufacturing process. Its ability to automate complex tasks, optimize production cycles and enable predictive maintenance offers clear advantages over traditional methods. ML increases efficiencies and creates cost savings in many industrial sectors by increasing asset uptime, increasing throughput, and enhancing decision-making processes.

While implementing ML has its own challenges, the tremendous benefits far outweigh the obstacles, and advanced analytics solutions can help ensure successful deployments. These software tools provide powerful data analysis capabilities and are specifically designed to handle the needs of time series data and ML applications in industrial settings. With user-friendly interfaces and a focus on collaboration, these solutions enable companies to fully adopt machine learning-based insights, delivering significant efficiency and profitability advantages in an increasingly competitive manufacturing market.

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