As global businesses move on to adopt the doctrines of Industry4.0, most industries are going through a radical transformation in terms of how they operate and maintain hardware/software assets. In Industry 4.0, advanced technology-based applications/platforms govern intelligent networking between humans and machines. The application of Predictive Maintenance is turning out to be a contributing advancement to this revolution.

Predictive maintenance of industrial machinery is a data-driven process of triggering proactive maintenance that restricts unplanned downtimes. In the long run, this strategy improves the efficiency and consistency in production/deliverability (depending on the type of business).

Predictive maintenance is the next big thing on the block. It is also widely associated with the Internet of Things (IoT) technology. If Predictive Maintenance is an automated method of analyzing machine health data and triggering action in real-time, IoT devices/sensors extract the relevant data for this process.

IoT nodes are data capturing devices attached to any smart gadget or machinery. These enable the acquisition of data from the machinery and establishes wireless communication compatible devices. A device, which is as simple as a light bulb to a piece of heavy machinery as a forklift can have IoT nodes installed in them. The IoT nodes can transmit the machine’s performance data in real-time. Utilization of this data comes handy in cross-examining variables, analyzing patterns, and automating maintenance processes before the point of failure. 

Industrial Internet of Things (IIoT) in Industry 4.0

Industry 4.0 leverages the usage of smart devices with IoT nodes and its sensors enhance device management and health assessment methodologies through an elaborate IIoT-enabled Predictive Maintenance platform. IIoT has the potential and proven excellence in maintaining quality control, detecting technical inefficiencies, and providing smart insights to guide maintenance and purchasing processes. IIoT-enabled Predictive maintenance is ideal for companies that are dealing with many technological assets.

IIoT nodes can enable Predictive Maintenance algorithms to forecast wear and tear in industrial machinery using varied data points such as vibration analysis, thermal imaging, and lubricant profiling. Beyond maintenance, IIoT is useful in cloud-controlling, tracking, and managing of technological assets.

Now, IoT has multiple areas of application in Industry 4.0. IoT-enabled Industrial machinery can be paired with a data-driven Demand and Inventory Management strategy as well. Maintaining the flow of production to keep up with the demand in the market is crucial, especially when the consumers are becoming less patient and the markets are becoming more competitive. An end-to-end IoT enabled industry is better equipped to handle steep demand changes in the market.

How is IIoT different from IoT? 

Although IoT and Industrial Internet of Things (IIoT) are quite similar, the prime difference lies in the purpose of usage. While IoT is used in industries such as Utilities, Advertising, Telecommunications, etc. IoT acts as a link between the product and the consumer; and between the consumer and the business. Like a fan or air conditioner equipped IoT technology can enable the user to operate the product remotely. Similarly, IoT enabled smart devices also provide the service centers to remotely assess the defect in the gadget and send the right technician for repair work.

IIoT technology’s application is more in the case of heavy industries such as automotive, manufacturing, shipment, oil and gas, etc. For example, Quality Control Managers in manufacturing industries can efficiently maintain the optimal machinery performance levels required for producing high-quality items and reducing the number of damaged items.  

What is the architecture of IIoT for Predictive Maintenance?

IIoT architecture in Predictive Maintenance refers to a set of digital progressions that triggers or drives the proactive maintenance processes. It can be broken down into 3 parts:

a. IoT Nodes- It begins with the collection of information from the piece of machinery through real-time analysis of heat, pressure, speed, flex, sound, etc.

b. IoT framework- All the IoT nodes are connected to the IoT framework that senses and correlates the data across the framework without much human intervention. 

c. IoT cloud server- The data is stored and processed using cloud computing and predictive analytics algorithms to deliver meaningful insights.

What are the challenges to IIoT-enabled Predictive Maintenance in Industry 4.0?

While the application of IIoT and Predictive Analytics in industrial maintenance strategies seems quite phenomenal, but it is a head-scratcher in the aspect of implementation. To accomplish this, businesses require technical expertise, that is either self-owned or channelized. Complete reliance on IIoT enabled Predictive Maintenance process can be devastatingly error-prone without human supervision. One cannot completely rule out the aspect of human intervention in this process as there needs to be a feedback mechanism to override and improve the model of data interpretation by this technology. 

Despite its widespread advantages, Cybersecurity in an IIoT architecture is the trickiest aspect to deal with. There is a high risk of data breach or security lapse in the IIoT framework because of outdated devices or weak data encryption standards in the framework. For an IIoT ecosystem to function seamlessly, a comprehensive security strategy is not only critical, but it’s also essential.

Whether a business is building an IIoT architecture from scratch, upgrading current machinery with IIoT technology, or involving a third-party vendor with IIoT expertise, this is a very expensive undertaking. If not implemented properly, this can potentially spike the operational costs and required man-hours.

Even after the implementation, it is a lost opportunity for businesses if they are just utilizing this elaborate IIoT enabled Predictive Maintenance set-up for just automation of the maintenance process. With smart utilization of an IIoT ecosystem, stakeholders and decision-makers might be able to gather exceptional insights to evaluate operations, resource planning, and man-hour utilization.

Regardless of the challenges, IoT is one of the most sought-after technologies that decision-makers consult Enquete for, and their concerns range from investment risks to seamless integration to secure interface to compliance measures. Businesses are betting hard on IIoT technology for long-term benefits. Global expenditure on IoT saw a growth of 14% growth in 2019. Despite the economic slowdown due to Covid-19, our studies suggest that the expenditure on IoT technology is set to reach a $1 Trillion milestone by 2022, with a significant push from the manufacturing and transportation industries. 

For a consultation on the implementation of IIoT enabled Predictive Maintenance strategy, reach out to Enquete’s Team of Data Scientists!

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