Customers demand instant solutions in today’s fast-paced world. With speed to market now no longer a luxury, but an essential requirement, enterprises are hard-pressed to offer an instant resolution to issues. One area where the pressure for speed is felt most is field service management. Machinery breakdown causes disruption. Each second of delayed resolution, not just results in direct revenue loss, but also a huge indirect opportunity cost loss.
Field service technicians leverage predictive analytics in a big way to diagnose machinery, to reduce resolution time, and even pre-empt the underlying cause in the first place.
Predictive analytics is the analysis of current and historical data to make predictions regarding future events. It involves a wide variety of techniques related to statistics and data mining. The application of such analytical tools empowers engineers and field service technicians with deep insights, facilitating accurate decision-making, and improving both process efficiency and productivity.
Today’s operating plants adopt multiple maintenance strategies. Adding a predictive analytic component to the mix helps to unearth issues which other maintenance techniques may not achieve on their own, or conversely provide early warning about imminent issues. In fact, it may be difficult to detect operating anomalies while monitoring equipment without advanced analytics.
Most predictive maintenance strategies involve continuous monitoring of sensor data, to evaluate the performance of the machinery or assets in question. The various predictive engines at work provide advanced warning of equipment problems and failures.
Advanced predictive analytics tools analyze raw sensor data from thousands of real-time streams. The application of pattern recognition and other diagnostic technologies for such data makes explicit deviations and patterns. The resultant insights may be applied to model equipment behavior, and/or alert engineers when an asset performs outside normal operation patterns.
A common technique deployed by any enterprises is Advanced Pattern-Recognition (APR). The APR methodology entails first scouring the asset’s unique operating history, to develop empirical models of performance under all available ambient and process conditions. Such models become the baseline to determine how the machinery operates. The analytic tool now analyzes real-time sensor data emerging from operations and compares such data to the baseline standards. The system flags even subtle changes in system behavior. Such anomalies are often early warning signs of impending equipment failure. Service technicians, engineers, and all other key stakeholders get immediate alerts, enabling them to act well In advance before the deviations reach critical levels. Maintenance teams can plan repairs well in advance, minimizing downtime. They also get enough time for analysis and planning any corrective action. For instance, they can order an unavailable spare part, without finding out about the unavailability when they get to inspect the machinery, and then lose precious days waiting for the spare part to arrive.
Anomaly identification is only one of the possibilities associated with predictive analytics. Once an issue has been identified, predictive analytics solutions can also offer root cause analysis and fault diagnostics, to help the field service technician or plant engineer to understand how the issue occurred in the first place, and how to pre-empt it in the future. The insights offered by diagnostic technology minimizes the chance of an attributing the fault to a wrong variable, improving the accuracy of the process.
Consider the case of a boiler feed pump, where the engineer receives a pattern recognition alarm during the winter season. The analytics may flag a higher than expected metal temperature for a bearing, factoring in the normal temperature for the season. The temperature reading in the Plant Process Computer may not flag an alarm for the same case, as it may be set for the maximum expected temperature typically occurring in the summer months. The predictive analytics tools further diagnose the alert and help the engineer to determine whether the anomaly is actionable.
Apart from executing various diagnoses and other tests, predictive analytics may be applied to identify what to test, and also to develop text metrics.
Analytic tools provide metrics in the form of heat maps, which is a key input for determining test coverage, to set functional, reliability, volume, and usability tests.
The defect data, unearthed by intelligent mining of Application Life Cycle Management (ALM) history help to correlate tests with cases resulting in the maximum defects, in past releases. The insights constitute a solid basis for creating a risk-based test matrix.
The application of predictive analytics in conjunction with productivity data helps to establish benchmarks, to evaluate the productivity of teams. Such data may also help managers and strategic planner identify the probability of meeting deadlines with a fair degree of accuracy.
Leveraging predictive analytics from operational production data help to build business SLA’s against critical business processes. This serves as a key input for performance modeling.
Security testing methods, such as penetration testing not only unearth new vulnerabilities, but also help to evaluate whether there is any existing vulnerability across firewalls, load balancers, server hardware, and other components of the network.
While the advantages of predictive analytics are obvious, the high costs of deploying sensors and other instrumentation essential for such analytics often serves as a dampener in the works. However, as technology continues to advance, and the price of sensors and other smart devices continues to decrease, predictive analytics are seeing increased adoption in enterprises.
The availability of cost-effective infrastructure is only one of the challenges. It requires talented, experienced, and resourceful hands utilize the infrastructure optimally, customized for the specific needs of the enterprise. Many enterprises also underestimate the amount of industrial big data and the magnitude of processing the same. It requires an experienced partner to master the process and transform raw data into actionable insights in real-time, with minimal costs.