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Facing Losses Due to Malfunctions- Predict & Prevent with IIOT

Manufacturing Industry is using Industrial Internet of Things to predict, prevent and decrease the number of process and system related malfunctions.

With the boom in Industrial Internet of Things (IIoT), more and more manufacturers and production professionals are eager to inculcate the new features enabling better safety measures and quicker solutions to maintenance related issues. Industrial accidents or safety malfunctions could be hazardous both on humanitarian as well as financial aspects.

If we talk specifically, IIoT enabled devices can help to ensure that workers follow proper safety protocols and adhere to instructions. Simultaneously, IIoT sensor collect data in form of real-time analytics and aggregate performance inform manufacturers or third-party services of past and present conditions to forecast maintenance issues before they occur.

Need of Predictive Maintenance for Manufacturing Industries

Ever evolving industry demands and increasing competition both at domestic and global level has made it necessary for medium as well as large scale industries to integrate predictive maintenance. Some major factors contributing to this change are significant decrease in maintenance costs, reduced emergency shutdowns due to mechanical failures, equal distribution of workloads with accurate goal predictions, reduction in manpower required for maintenance activities, better management of inventory and spare part requirements, lesser interruption in scheduled tasks due to random issues and longer life of equipment by decreasing damages due to maintenance delays.

Trending Technologies

To have better perspective of the ways in which IoT is enabling better safety practices, let’s have a look into the trends of preventive maintenance and predictive troubleshooting within the manufacturing sector.

IBM Watson, the cognitive intelligence engine with its Predictive Maintenance and Quality (PMQ) is one of the leading players in the market of predictive maintenance. It has capability of monitoring, analysing the reports on equipment data and generate a detailed health report of instruments with the required maintenance log. Kone Elevators is a known user of this technology by IBM.

SAP with its huge presence in software market is also providing predictive maintenance services. It enables closed-loop maintenance and service process by optimising asset maintenance with anomaly detection, spectral analysis, and machine learning algorithms. It can also monitor connected devices and support IoT data transfer services to optimise data management with scalable and cost-effective storage for time-series data.

GE has adopted two sided approach towards Predictive Maintenance program in which GE Measurements is its subsidiary which covers the condition monitoring hardware field, whereas GE Digital covers the software and analytics part of Predictive Maintenance. GE has implemented Predix Asset Performance Management (Predix APM) with BP’s oil and gas production operations.

Better known for its tools and manufacturing solutions, DEWALT now claims that with its new IOT integrated platform tools they will be able to answer questions like “Who used that power cutter last and where did they put it?” or “Which tool’s drill bit has been overused?” These are the kind of questions that supervisors working on building sites often need answers of. This company proposes a planned WiFi mesh network which will feature ruggedized access points built to withstand the harsh environment of a construction jobsite.

This will also allow contractors to collaborate in real time and access critical site information such as schedules, budgets and requests for information (RFIs). The network will then communicate to Dewalt IoT platform, which will let supervisors identify the location of tools on-site and get data into how they are being used.

Ergonomic solutions company StrongArm Technologies has introduced a vest with integrated sensors that shifts heavy loads from injury-prone parts of the body to stronger areas. The V22 also helps the users in adopting proper lifting positions, helping them avoid any potential injuries. This vest could be extremely helpful to industrial workers who take up heavy loads and minimise the chances of injuries in case of accidents.

Market Growth Statistics Forecasts US$10.96 Billion by 2022

Photo credits: IoT Analytics Research

A recent Predictive Maintenance report by forecasts mentions:

  • A compound annual growth rate (CAGR) for Predictive Maintenance of 39% over the time frame of 2016-2022
  • The annual technology spending reaching US$10.96 Billion by 2022
  • Predictive maintenance could result in 20-30% efficiency gains if used to optimize internal operations
  • These numbers are based on the Predictive Maintenance related revenue of 110 leading technology companies in the field, across 13 industries and 7 technology areas.
  • Major companies involved were Accenture, Alexander Thamm, Bosch, Caterpillar, Cisco, Dell, General Electric, Helium, Huawei, Hitachi, IBM, Keysight Technologies, Microsoft, National Instruments, SAP, Schneider Electric, Siemens etc.

Challenges in Implementation of PdM

Like any other disruptive innovation, the IIoT based PdM will present multiple challenges to adopting enterprises. For instance, due to the large amount of data generated by IoT machines the main data server centres will face challenges in data management, privacy and security, data mining and standards determination.

However as stated earlier, given the IIoT as well as PdM are young kids on the block, we all will have to wait for studies on the social, behavioural, economic, and managerial aspects of the IoT. This makes it very challenging for companies to take informed decision.


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