IIoT-based predictive maintenance detects disruptions before they occur. The digitization of manufacturing processes results in networking the associated machines, production systems, and tools. This also affects maintenance. While preventive care has dominated many areas to this day, as technologies become cheaper, so-called predictive maintenance concepts are also spreading. However, machine builders who want to offer their customers added value based on the new maintenance approaches must switch to data-based business models. These use integrated IIoT platforms to collect and analyze data from networked machines and systems.
The Industrial Internet of Things (IIoT) is turning numerous industrial sectors upside down. In mechanical engineering, the networking of machines and systems generates data with great potential that industrial companies can use to optimize their production processes, for example. Networking allows local machine maintenance to be expanded to include centralized data analysis, from which, for example, the expected failure time of a component, such as a seal or a bearing, can be derived. By comparing the machine and system data recorded during operation with other data, such as idealized models, the software uncovers errors and faults as they arise. Often long before the incident occurs.
Anticipate And Delay Maintenance Events
IIoT platforms enable continuous 24/7 monitoring of machines and systems in real-time. Intelligent sensors integrated into production machines collect the data generated during production and send it to a cloud-based IIoT maintenance platform. This prepares them and enables trained users to conclude faults in the system from the recorded noises, speeds, or temperatures.
In the event of service, technicians can use the collected system data to troubleshoot in a targeted manner. A machine model enriched with historical data can anticipate maintenance events and delay them to an optimal point by automatically changing process parameters. The consequences are reduced maintenance cycles and maintenance times.
Automatic Alarms When The Limit Is Exceeded
A wide variety of systems and machines, ranging from production systems, wind turbines, or aircraft turbines to printing presses, motor vehicles, or cranes, can be monitored and maintained worldwide using predictive maintenance via the Internet. Communication usually starts in networked systems, where sensors, measuring stations, or probes record and transmit conditions such as temperature, vibrations, utilization, or wear.
Product and service experts set specific limit values that must not be undershot or exceeded for the evaluation. If this is the case, the system automatically triggers an alarm and sends a notification, often by email or SMS. A crane manufacturer, for example, defines wind speed limit values.
Lifetime Determination By Acoustic Patterns
An analysis method frequently used in predictive maintenance is called acoustic pattern recognition. The service life of a specific part or component, such as a valve, can be determined based on changes within an auditory pattern. Using artificial intelligence (AI) and machine learning, complex measured values are assigned meanings based on which data scientists can assess. For example, the current state of wear of the drill can be read from the vibrations of a table in a CNC machine. Is it new, already worn, or already worn out? More precise predictions are also possible, such as: “The drill has reached 15 percent of its lifetime.”
Efficient production is dependent on the functionality of your plants and technical systems. At least 95 percent of the possible operating time, technical availability is ideal. As part of predictive maintenance measures, automatic detection of frequently occurring errors can be implemented in the machine—for example, identifying encoder errors in sensors or deviations in machine calibration. Thanks to the data-supported, continuous, and always up-to-date insight into the system used, potential for improvement can be identified and implemented at an early stage, for example, by comparing it with a digital model, and the availability of the machine can be increased.
IIoT Platforms Must Be Able To Be Integrated Into A Wide Variety Of IT Systems
However, to digitize the production processes and manage them appropriately, suitable IIoT platforms are required to give users direct, uncomplicated access to a plant’s operating and status data. For this to work, they must be able to be seamlessly integrated into the IT systems of a wide variety of manufacturers. Modern IIoT platforms provide a standardized API that users can use to access machine data and analyze results.
REST or GraphQL-based APIs, in particular, have proven themselves in this context. The more successful an IIoT application is, the greater the amount of data that needs to be processed over time. Platforms that must be set up to manage ever-increasing amounts of data will quickly reach their limits. When purchasing a platform, companies should pay attention to its scalability. Their services – from messaging to the database to API services – must be able to be executed in parallel so that a seamless increase (or reduction) of resources is possible.
With data, IIoT is possible. But only with trained employees who are familiar with the handling and analysis of data. Specialists such as data analysts or data scientists are far too seldom to be found in medium-sized companies these days or are often unavailable. However, without employees with qualified data expertise, it will be difficult for companies to create value based on their IIoT platform.
Low-Code Platforms As Game Changers
For this reason, more and more providers are offering IIoT platforms based on low code. Instead of using classic text-based programming languages, low-code media support the development of processes with visual user interfaces and other graphical modeling techniques. This makes it possible for users who have a great deal of machine expertise but need more IT knowledge to configure their applications and apps themselves and independently evaluate the status data of their machines and systems without professional programming knowledge. Predictive maintenance based on low code is a game changer for mechanical engineering.
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