How Predictive Maintenance Is Improving Equipment and Machinery

Predictive maintenance is a scheme that justifies the condition of operative equipment in companies. Its work is based on analyzed data from the equipment, providing information on the equipment’s next maintenance. Predictive maintenance is a solution that can be executed via the Internet of Things (IoT) by providing data from equipment such as changes in its conditions or engine hours. One of its aims is to verify the equipment at an appropriate time to help in rectifying problems. Moreover, it is a cost-reducing, condition-based maintenance solution for companies to increase productivity, expand the service life of the equipment, enlarge plant safety and optimize the use of spare parts.

Predictive maintenance reduces the number of unpredicted failures and provide a better way for scheduling preventative maintenance function. Following are some of the reasons that perk up the predictive maintenance market:

• Reduction of maintenance cost: As compared to the maintenance cost by other procedures, including maintenance department and labor along with the price of replacement tools and other required equipment for maintenance, predictive maintenance has been noticed to reduce the maintenance cost up to certain extent.
• Lowering machine failure: The unexpected failure of equipment can be prevented by regular monitoring its condition and process systems. Moreover, predictive maintenance could help reducing the rate of machine failures.
• Expand the service life of the equipment: The prediction of the problems of machines and systems, frequency of repairing them, and condition of the machine after repair increase the service life of the equipment.
• Validation after repairs: Vibration analysis is a technique used to identify the positive and negative changes in the machinery after it gets repaired, without the need of the second shutdown.

Along with the increase in adoption of predictive maintenance, certain companies have also come up with innovative ideas that involve the condition-based solution. Following are some of the initiative taken by the companies:

BISTEL introduces cloud-based IoT equipment for predictive maintenance and health monitoring:

BISTEL, a Belgium-based company, has announced a cloud-based IoT equipment as predictive maintenance and health monitoring solution. It will provide constant monitoring of health and can monitor the performance of other equipment and machinery, providing data visualization for all the users. Moreover, the company plans to demonstrate the product to automobile, pharmaceutical, and biotech users.

WinGD merges with ETH Zurich to enhance predictive maintenance:

A leading technical university, ETH Zurich along with WinGD (Winterthur Gas & Diesel) low-speed gas and diesel engines developer for marine propulsion, has developed an advanced algorithm based on the physical- and data-driven model to boost predictive maintenance for marine two-stroke engines. The solution will be further tested and verified for executing over the future versions of the WinGD Integrated Digital Expert (WiDE) engine data analytics system.

What’s more, the investment in predictive maintenance has risen. According to a report published by Allied Market Research, the predictive maintenance market is estimated to reach $23.01 billion by 2026, growing at a CAGR of 30.20% in the forecast year. It is accountable for the reduction of maintenance cost of the equipment and uptime improvement. On the other hand, the use of IoT devices, machine learning, and in-time condition monitoring are providing profitable opportunities for the market.

 

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