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7 Powerful Use-Cases of An IoT based Predictive Maintenance system

by Rajesh Shashikant Renukdas, on Mar 9, 2021 4:07:43 PM

IoT has penetrated every industry and every device connected through the internet, and mobile capabilities have further accelerated its foray with accessibility to data on-the-go and abilities to drive and implement actionable insights with this data in just a few taps. With advanced mobile use cases driven by an IoT empowered back-end, enterprises are unravelling a whole new paradigm of productivity and intelligence embedded within the enterprises.

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In this blog, we will take a look at how enterprises can proactively leverage IoT-driven mobile-use cases to implement next-gen asset management, and realise numerous advantages through automation across processes. Let us begin with a brief background of the agenda here:

An Overview

IoT based predictive maintenance uses statistical process control principles to determine the best time to perform asset or machine maintenance across industries. It can also help increase safety and reduce the number of accidents in a factory plant by early detection of faulty equipment or design flaw in a machinery. An operator centric mobile/web user interface can function as a central control unit for management of this data. Data is processed according to set rules and is further made available in real-time to the users. An app can facilitate report generation and integrate numerous customizable plugins as per the requirements. Following are a couple of industries where IoT-based mobile-backed maintenance is growing traction:

  • Automotive: The industry depends on the efficiencies and performance-cost ratio the of vehicles. To minimize the downtime and increase the TAT for response, predictive maintenance algorithms can be applied to data gathered through vehicles to identify maintenance issues before they happen.
  • Airlines: Predictive maintenance helps industries like airlines to keep track of the airplanes, and project system or parts failure to take necessary measures in time.
  • High tech manufacturing: Predictive maintenance using IoT enables factory workers working under parameter value constraints to operate at levels closer to optimal and updates the condition regularly.
  • Railways: Sensors like thermal, visual, force etc. are actively used in railways to identify prospective failure points in braking capabilities of freight-car, high friction in wheels as well as bearings, straightaways damages etc.
  • Electric Power: Power plants are under a mandatory obligation to ensure sufficient power supply. A predictive maintenance solution here helps power manufacturers to ensure hassle-free power generation and identify evolving defects in a gas/steam turbine components of rotation.

Here are some actual use cases of the IoT based mobile UI that can help enterprises streamline their asset management:

1. Monitoring pressing quality

Process data in the pressing management systems in manufacturing units can be extracted through sensors and sent across a mobile/web interface for constant monitoring of the force as well as the location of the processes. This data can further be used to lay down a blueprint of the process which can ultimately serve as the reference for all pressing processes across a plant. This also enables a direct identification of flaws in the pressing process, according to the data fed into the system.

2. Monitoring hydraulic valve lubricants & filters

The oil cleanliness level used to check hydraulic valves post the production is expected to meet a specified international standard. To make sure the systems pass the quality standards, oil quality is supposed to be improved. Test branches can be retrofitted with IoT sensors that communicate with machines and send data to a mobile/web based front-end where it is deployed to drive actionable insights. A protocol-based monitoring of the standard cleanliness levels of oil can be easily leveraged through the system.

3. Monitoring the tightening processes

Incases where the production plants are situated at various remote locations, it can be hard to track the consistency of the quality across all units. Nut runners can be connected in various unit locations to a centralized system. This makes sure that consistent quality standards are applied to all the units.

4. Maintenance ticket allocation

Centralized management for machines, can help display alerts on the local system. This means that workers no longer have to wait besides a machine to overlook the issues and can be deployed elsewhere. A mobile app can also help stakeholders automatically raise maintenance tickets for workers based on the requisites. The workers can access the details with an app that alerts them with push notifications in case of an event.

5. State monitoring of cooling systems

Cooling systems are composed of pipes, and blockages in these pipes can lead to pump failure. Such a problem can be encountered when there is no mechanism to centrally monitor cooling performance regularly. The temperature as well as the flow sensors can be connected to the cooling systems to accumulate data, this data can further be used on the front-end to define limits for cooling power and flow.

6. Quality monitoring in painting processes


Painting of queer-shaped objects like car windshield and similar objects can be daunting. Through an IoT gateway into the process, production data can be collected on the front-end and can be used to identify the critical parameters of quality such as temperature, paint quantity etc. Following this, threshold values can be set and alerts can be sent on mobile apps in cases where quality or quality parameters are not inline

7. Vibration analysis of milling machines

By locating sensors in closer proximities of milling machines, data can be accumulated about certain vibration patterns through cutting operations such as milling and drilling. The process data when accumulated overtime can offer a unique threshold for each milling process. By comparing the recorded threshold with these values, one can drive differences in processes and make required updates in the process.

Conclusion

There is no dearth of use cases when it comes to implementing IoT in industrial units for asset management. However, a robust mobile/web framework that can help handle huge chunks of data across infinite processes becomes critical for all industries. It is therefore advisable for enterprises to work in tandem with mobile technology consultants to implement IoT predictive maintenance applications, with sufficient industry experience to begin their journey with this remarkable Industry 4.0 technology.

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Topics:IoT predictive maintenance applications

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