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Predictive Maintenance with IoT and Data Science

Manufacturers have been carrying out equipment maintenance using a time-based strategy for years. In the past, they planned the maintenance schedule based on the age of the apparatus. The requirement for more frequent maintenance procedures increases with the age of the equipment. However, according to a survey, 82% of equipment failures occur at random and just 18% of equipment failures worldwide are related to aging. It demonstrates why a time-based strategy is inefficient financially because equipment is maintained regardless of its true needs.

Manufacturers may use data science and Industrial IoT to prevent inefficient maintenance procedures and the associated expenses.

In this article, we’ll discuss how IoT-based predictive maintenance enhances manufacturing process optimization and how pg in data science can help you excel in this field.

What is Predictive Maintenance?

Utilizing both historical and current data, predictive maintenance is a data-driven methodology that attempts to predict equipment faults. Preventing premature replacements and unplanned breakdowns is its main goal by scheduling maintenance tasks when they are most needed.

Traditional preventative and corrective maintenance techniques can result in unneeded downtime and higher expenses. IoT-powered predictive maintenance completely changes the game by enabling manufacturers to fix problems right away, resulting in lower costs and more efficient operations.

What is IoT Predictive Maintenance?

The Internet of Things is transforming manufacturing (IoT). Using IoT technology for predictive maintenance, which focuses on proactive planning for equipment breakdowns and helps firms maximize resource use, is one of the biggest benefits.

IoT-based predictive maintenance systems use software analytics to create reports on possible issues or failure risks, and data-collecting sensors to learn about operating conditions from machines and equipment in order to achieve this level of predictive maintenance. With the help of this trustworthy data, manufacturers can better plan the allocation of resources by knowing when maintenance is needed and which parts might need to be replaced. In turn, this can help manufacturers predict future requirements more quickly and avoid costly repair expenses.

Why IoT?

Large-scale data processing and complex algorithm execution are necessary for predictive maintenance, and these tasks cannot be completed locally within SCADA. On the other hand, an Internet of Things (IoT)-based solution, such as a smart factory app, enables the storage of terabytes of data and the parallel execution of machine learning algorithms on several computers to predict potential risks and identify the likelihood that industrial equipment would malfunction.

Benefits of Predictive Maintenance?

By using predictive maintenance, manufacturing equipment’ performance can be increased, their states and defects may be predicted, maintenance schedules can be set, and their lifetime can be estimated based on the analysis and processing of the data gathered. When an operator performs a defective act, the system evaluates the performance and promptly notifies the operator of the incorrect act. It is anticipated that using the suggested method will prevent malfunctions, cut down on lost time, require less work, have a lower operating cost, and be able to detect failure before it happens. The most significant outcomes are a longer machine operational life, lower spare part costs, less production downtime, higher-quality products, and energy savings.

Benefits of IoT Based Predictive Maintenance

  • Cut down on maintenance expenses

  • With the help of sensors, analytics, and machine learning algorithms, IoT-based predictive maintenance may anticipate when a machine or other piece of equipment will need maintenance, saving businesses money by minimizing downtime as well as maintenance expenses. Predictive maintenance based on IoT is a potent instrument that can assist businesses in reducing expenses and increasing overall productivity.
  • Boost the use of assets

  • IoT-based predictive maintenance helps businesses make better use of their assets by anticipating and averting equipment breakdowns before they happen. Businesses can gain important insights into possible difficulties and take proactive measures to remedy them by collecting data on equipment performance using sensors and other Internet of Things (IoT) devices. This helps minimize expensive repairs and downtime while also extending the life of equipment and increasing overall productivity for enterprises. With the assurance that their assets are constantly operating at maximum efficiency, organizations can stay ahead of the curve thanks to IoT-based predictive maintenance.
  • Boost technician productivity

  • By offering real-time data on equipment performance, IoT-based predictive maintenance can help technicians identify possible problems before they become serious ones and schedule maintenance at a time that is convenient and economical. This can also increase technician efficiency. Businesses can free up technicians to work on other projects by using this strategy to cut down on the time and resources needed for maintenance.
  • Minimize equipment failures

  •  Equipment downtime can be minimized by identifying problems early and planning maintenance at a suitable and economical time. The manufacturing, energy, and transportation sectors are seeing a sharp increase in the use of IoT-based predictive maintenance to minimize equipment downtime and streamline operations. Businesses may reduce unplanned downtime costs and extend the lifespan and overall efficiency of their equipment by using sensors, data analytics, and machine learning algorithms to anticipate equipment breakdowns before they happen and schedule maintenance proactively.
  • Boost safety and compliance

  • By keeping an eye on equipment in real time and making sure it’s always in excellent operating order, IoT-based predictive maintenance may also be a potent tool for boosting safety and compliance in a number of industries. Once possible safety hazards have been identified, action can be done to resolve them before they become a risk to public safety or cause equipment standards to be violated by regulations.

Conclusion

Predictive maintenance using the Internet of Things (IoT) has become a powerful way to improve maintenance procedures in a number of different industries. Predictive maintenance enables organizations to proactively schedule maintenance tasks, identify possible problems before they arise, and monitor the state of their equipment using real-time data from linked devices.

IoT devices make it easier to gather large amounts of sensor data, including pressure, vibration, temperature, and other pertinent variables. This data can then be examined by sophisticated analytics and machine learning algorithms to identify trends, abnormalities, and early warning signs of equipment deterioration or malfunction. Explore more data science courses.

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