Predictive maintenance – Data engineering’s role in reducing downtime in manufacturing

By Vipin Vindal, CEO, Quarks Technosoft

The manufacturing sector is undergoing a remarkable transition prompted by analytics and cutting-edge technologies. In today’s digital age, where data holds immense significance, efficient data collection, storage and management have become imperative for organisations to succeed. Downtime is one of the greatest challenges confronting the manufacturing industry. Any time when facility output is halted is referred to as manufacturing downtime. It includes both unplanned downtime spurred on by events or equipment breakdowns and planned downtime for regular asset maintenance. A shortage of workforce or resources can also cause stoppages in production. According to Sapio Research, around 88 per cent of Indian industrial enterprises have countered unanticipated outages at least once a month in 2023, costing them almost INR 7 million per hour of downtime. Fortunately, the implementation of best practices in data engineering and real-time monitoring has not only helped manufacturing companies achieve a measurable reduction in downtime but also reassured the industry about its progress in maintaining excellent quality control.

Predictive maintenance: A closer look
Predictive maintenance, or PdM, is a subset of data engineering that estimates machinery maintenance requirements. It discovers, anticipates, and helps prevent the fundamental causes of disruptions, thus increasing overall productivity in the manufacturing industry.
Predictive maintenance analyses operating data and generates patterns, allowing operators to foresee any failure in mechanisms and predict maintenance requirements while lowering overall costs. A key aspect of data engineering, PdM utilises numerous technologies to create a robust solution for an operation in the manufacturing industry.

These technologies are:
Infrared thermography: Heat is an inevitable outcome of the manufacturing process. PdM platforms can map heat patterns and identify potential concerns both automatically and manually. Infrared thermography can detect and quantify temperature in equipment such as motors, bearings, and friction surfaces. It may also help find “hot spots” in electrical wiring cabinets and identify insulation failures.

Acoustic monitoring: Acoustic monitoring, a predictive maintenance monitoring system, employs high-frequency signals or airborne sounds to assess the equipment’s condition. By recording such acoustic noises, breakdowns can be recognised. In such cases, advanced machine learning algorithms are employed to improve prediction accuracy. These analytics can also be used with other monitoring technologies to examine data deeper and identify abnormalities before they aggravate.

Vibration analysis: This technology  assists predictive maintenance engineers in understanding what small and large changes signify. They may measure wear rates and failure spots as machine learning algorithms advance in accuracy over time.

Oil analysis: While predictive maintenance platforms can evaluate the health and potential failure of components or equipment via sound, vibration oil analysis has formed as a different approach to record internal machine activity. With this process of testing oil purity, debris contents, pollutants, and oil composition, specialists can identify, map out and forecast the cause of the problem; creating corrective measures. The results from the oil analysis can then be transferred to the analytics platform and integrated with other monitoring data to provide a comprehensive picture of machine health.

Predictive maintenance: Use cases
Below are some use cases of predictive maintenance applied to various processes and equipment in the manufacturing industry:
Pumps, motors and conveyor belts: A manufacturing company can use vibrational analysis to detect wear and tear on pumps, motors and conveyor systems, allowing it to anticipate when maintenance is required. Heating, ventilation and air conditioning systems (HVAC): A company may employ infrared analysis to monitor equipment temperatures and predictive analytics to establish the optimal maintenance plan for its equipment.

Quality control: PdM can be used in the manufacturing industry to identify defects in goods before they leave the production unit. By analysing data from machine logs and sensors, this method can eventually provide maintenance managers with alerts when a malfunction is most likely to occur. Taking these use cases and benefits into account, Maximise Market Research has estimated that the Indian predictive maintenance market will reach USD 4 billion by 2026.

Wrapping up
Data engineering has become pivotal in driving a data-driven revolution across the manufacturing industry. While reducing downtime and optimising a maintenance strategy has been challenging for manufacturers, predictive maintenance has emerged as the most efficient method for accomplishing the goals. PdM can now assist manufacturers in determining the ideal time to perform maintenance operations, notifying them of prospective downtime, thus extending the usable life of the equipment. As the manufacturing industry continues to embrace digital transformation, predictive maintenance has become not just a competitive advantage but also a need for long-term success.