In the global manufacturing market, success means delivering a near-perfect product to customers. Machinery is the life of a manufacturing unit. Moreover, operators play a vital role in the production process by assuring the maintenance of the machine. However, burdening an operator for assuring the quality of every piece of machinery can be a call for disaster. To overcome this problem, manufacturing leaders are betting on the new age technology. Like any other industry, manufacturing leaders are keen to use an intelligent system that can scan the surface of machinery, list and analyze the problems without human intervention. Let’s take a look at how different types of predictive maintenance technologies are transforming the production process in manufacturing industries.
Predictive maintenance technology helps manufacturers predict a potential failure of critical machinery with high accuracy. But how does the system work? It works with the Internet of things (IoT) sensors. It can record heat, pressure, and triaxial vibrations. Moreover, the operators set up the sensors on the machinery. Eventually, the system transfers the data to the public cloud. In the data lakes within the public cloud, the algorithm compares the present data with the past data. The data signals failures of the equipment in terms of their correlation with numerous other factors. We have listed 4 types of technologies used in predictive maintenance:
Infrared inspections can spot defects and help dodge costly repair and maintenance. The analysis of thermal data over the equipment can chart a pattern for future machine maintenance and handling strategies.
This technique is especially used for rotating equipment. Generally, when the equipment is in a stable condition, it exhibits a standard vibration. However, the analysis of vibrations can only help the operator to examine the degree of misalignment.
A worn and unlubricated bearing generates a type of frequency that can be captured by the system. As a result, it can predict the gas, liquid, or vacuum leak in equipment.
Oil data analysis can reveal a myriad of results such as the viscosity of oil, presence of contaminants, particle counts, and the acid number or base number.
Lastly, no industry is immune to machine disasters. Drug companies, technology titans, electronics manufacturers. They’ve all faced disorganization due to defective equipment from supply chain productions to big manufacturing factories. The aftermath can be adverse: millions of dollars in lost revenue, government sanctions, lawsuits, and consumer mistrust.
Revti Vadjikar is a digital marketing associate who creates and distributes compelling stories about data science. She creates engaging blogs, case studies, visual and video content for US-based businesses operating in a variety of industries. She is an engineer who is passionate about reading non fiction stories