With digital transformation being embraced by industries, and exponential growth in the fields of Artificial Intelligence (AI) and Machine Learning (ML), businesses are changing the way they operate.
In the manufacturing sector, the Industry 4.0 revolution is bringing in many changes. Advancements in automation technology and large-scale adoption of IoT devices in industrial settings is leading to a data explosion, which can be used to increase speed, flexibility and production in manufacturing facilities. Machines continue to play a key role, and their breakdowns or downtime can halt production and incur heavy costs. According to the International Society of Automation, downtimes cost $647 billion globally every year and Gartner estimates a loss of $540,000 every hour of downtime. To avoid these maintenance costs, adopting AI and ML enables the manufacturing industry to carry predictive maintenance at optimal costs by conducting a thorough analysis of the data.
How Artificial Intelligence Conducts Predictive Maintenance in Manufacturing
Maintenance of machinery using historical data and following a set routine to reduce downtime and optimize performance has been a decades old practice, usually requiring the participation of both machine operators and machine technicians to carry out inspections and repair. While machine operators carry out regular maintenance of equipment, technicians have the necessary historical machinery knowledge to carry out repair jobs comprehensively. Planned maintenance, though preventive to avoid unplanned downtime and carried out while machines are working, are still in response to events or timeframes that call for repair work. Following routine without the aid of contextual information may also lead to under-maintenance or over-maintenance, neither of which are desirable.
At the same time, every piece of equipment may have its own unique set of data necessary for diagnosis and calibration and it may not always be clear what data should be used as a diagnostic data stream. ML can be a cost-effective approach for iterations required to identify data that can be used for the detection of failure patterns. Data necessary for predictive maintenance collected using sensors include temperature, humidity, pressure and vibration, which then need to be measured and analyzed. Even when SCADA (Supervision and Data Acquisition) systems are used to collect data, visualize, and trigger alerts, it requires a lot of human intervention for operation. Manually interpreting this data in real-time to detect anomalies is exceedingly difficult. AI and ML can analyze the data in real-time and detect failure patterns, preventing downtime and optimizing production.
Data Types in Predictive Maintenance
Predictive maintenance of pieces of equipment would require experimentation to determine which combination data can be helpful to predict machine failure. The data types that can be used in predictive maintenance, include:
- Computer Vision Data: Information collected by a camera as it watches equipment function and does a qualitative assessment of products on the assembly line. This data can be used to monitor the health of the machine based on its functioning.
- Programmable Logic Controller (PLC) Data: This data captures the human and machine interaction and can be used to analyze human inputs and the machine’s output.
- Sensor Data on Equipment: This includes an important set of data on heat, humidity, vibrations, etc., all of which have a contributory role in machinery breakdown. ML can be used to analyze this data, along with other data types to detect failure patterns and prevent machines from breaking down.
- Historical Data: Data on previous malfunctions and breakdowns can be used along with sensors to accurately assess the current state of the machine and determine when it is likely to develop similar issues.
- External Data: The more data there is the greater chance of analysis being accurate. Data from other equipment and general environment, such as weather data, can be used in combination with other data types to measure impact on equipment’s functioning.
How Predictive Maintenance Can Help Companies
In the age of Industry 4.0 and the resulting abundance of data, artificial intelligence and machine learning can help sift through data and gain meaningful intelligence and actionable insights. It can also find relationships between different sets of data, such as historical data and current readings to identify risks of failure without human intervention.
Predictive maintenance can not only streamline maintenance and reduce downtime of machines with lesser involvement of machine specialists, but it can also prevent catastrophic failures, which can adversely impact production. Predictive maintenance can reduce costs significantly, increase speed and flexibility, and boost production in manufacturing industries.