What is Maintenance 4.0?

Industry 4.0, also known as the fourth industrial revolution is a mind boggling trend of automation and data interconnection that has taken the world by storm. It includes the Internet of Things (IIoT), Wireless Sensors, Cloud Computing, Artificial Intelligence, Machine Learning, and Big Data.

The primary effort of Industry 4.0 initiatives is to connect machines, operators, and processes through technology. Smart factories are equipped with advanced sensors, robotics, and embedded software, that collect and analyze data to allow for better decision making. 

Even higher value is created when data from production operations is combined with operational data from ERP, supply chain, customer service and other enterprise systems to create whole new levels of visibility and insight from previously siloed information.

This leads to increased automation, predictive maintenance, self-optimization of process improvements and, above all, a new level of efficiency and responsiveness to customers not previously possible. 

Maintenance is one aspect that is streamlined by industry 4.0 solutions. Using sensors, IIoT, Big Data, Artificial Intelligence, and other intelligent systems, we can quickly identify where failures are occurring. It is possible to find out which equipment is being affected, how these problems can hamper productivity, and the best preventive maintenance plan to counter failures.

Generally, when it is necessary to collect data on the state of the machines, we turn to technicians. With the rise of Maintenance 4.0, these tasks can be performed by technology, which extends the useful life of machine components and minimizes unplanned downtime. 

Maintenance processes transform from a preventive to a predictive model, shifting the emphasis from prevention to forecasting. For example, leveraging Big Data alongside Artificial Intelligence, allows us to accurately determine the useful life of equipment, the risk of failure, and the respective impact on the system.

That being said, let’s explore the key components that make up a successful predictive maintenance system:

Preventive Maintenance

We’ve all heard the saying, “Prevention is better than cure”! Preventive maintenance quite simply prevents the failure of assets and equipment before they occur. This type of maintenance is carried out at regular intervals, even if the assets do not display any signs of damage, to ensure uninterrupted operations and safety of assets.

Predictive Maintenance

Predictive maintenance is a technique that uses data analysis tools and techniques to detect anomalies in your operations and possible defects in your equipment and processes, so you can fix them before they result in failure.

In a nutshell, predictive maintenance uses historical and real-time data from various parts of your operation to anticipate problems before they happen. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation.

It offers a range of benefits including, increased ROI, a reduction in maintenance costs, a decrease in breakdowns, and a significant drop in downtime. 

Big Data

Big Data refers to the large volume of data – both structured and unstructured, that is collected during a production process. Big Data Analytics can help you unlock valuable insights that enable better business decisions. 

Artificial Intelligence

AI’s goal in predictive maintenance is simple: Analyze massive real-time data speedily to predict assets failure intelligently, so that manufacturers can keep their mission-critical assets running at peak performance.

   To read more: 4 Ways Artificial Intelligence Is Transforming Manufacturing 

Internet of Things (IoT)

The Internet of Things is having a profound effect on the manufacturing sector, leading to increased automation, more efficient operations, and the creation of valuable new business models. While the application of digital technologies can bring benefits across the value-chain, it is arguably in the area of predictive maintenance that the most significant impact can be derived. 

The use of sensors and data analysis means companies can spot patterns in equipment condition and performance, and accurately predict when a failure might occur. Such foresight eliminates unplanned downtime, delivering substantial productivity benefits.

Cloud Computing

Cloud computing refers to the provisioning of computing services, including servers, storage, databases, network, software, analytics, and intelligence over the internet (the cloud). 

It enables you to receive, collect, process and display the data that comes from sensors effectively. 

In a nutshell

Be it Manufacturing, Construction, or Logistics every industry has mission-critical equipment that needs to be maintained perfectly for uninterrupted production. However, unplanned downtime of the critical equipment could impact productivity and prove detrimental to operations. Such unexpected asset failures can also skyrocket maintenance costs.

Our AI-powered predictive maintenance solution makes it easy to monitor equipment health in real-time and predict failures. It can effectively help you prevent unplanned machine failures, increase equipment, and production line productivity, and reduce maintenance costs.

Ready to transform your maintenance? Talk to our experts today!