Dealing with plant malfunctions is part of the daily routine in a production facility. However, the reporting process in many companies is treated step motherly and ranges from telephone reports to paper lists to complex entries on a central screen. The latter in turn leads to reports not being made via the system, but via telephone.
Measures for Digital Maintenance
As a result, a lot of valuable data is not available to the company, although it is actually supplied almost free of charge and could even lead to condition-based monitoring of the systems through clever linking and analysis.
So how can digital maintenance be better addressed and what measures do companies need to take to achieve these business objectives?
Digital maintenance is a crucial aspect of modern industrial operations, aiming to optimize maintenance processes through the effective use of digital technologies. One of the most powerful tools in this pursuit is AI-based industrial analytics, particularly predictive maintenance. By leveraging artificial intelligence algorithms and machine learning models, companies can transition from reactive maintenance approaches to proactive strategies, significantly reducing downtime and enhancing overall equipment reliability functions.
The simple digital message
Simple – the keyword.
The first step is the simple and fast reporting of a malfunction on a machine by the worker: intuitive and easy-to-use apps on a smartphone are the solution for the first step.
Simplifying maintenance reporting and data collection is vital for enhancing digital maintenance practices. AI-based industrial advanced analytics solutions offer a straightforward and efficient way to report malfunctions on machines. Intuitive smartphone apps equipped with AI-driven features empower workers to generate informative messages with just a few clicks. Through artificial intelligence integration, detailed reports with time stamps, machine information, asset data, and malfunction descriptions are automatically generated, streamlining the reporting process and ensuring valuable data is readily available for equipment data.
The maintenance department now has all the information and by linking to other existing data, the stock of spare parts can be viewed immediately, or orders can be triggered semi-automatically if required. At the same time, the order can be used to book the expenses with the necessary account assignments.
Predict failures and transmission of the status in real time – it could hardly be simpler or faster.
Digital Transformation in Maintenance management
Digitalization is reshaping industries, and digital maintenance plays a pivotal role in this transformation. To unlock the full potential of digitalization, companies must embrace AI-based predictive maintenance as part of their initiatives in computerized maintenance management system. Advanced Analytics solutions powered by artificial intelligence enable businesses to extract valuable insights from vast amounts of data collected and accelerate digital transformation. This integration facilitates condition-based monitoring, where real-time sensor data is continuously analyzed to anticipate maintenance needs and initiate timely interventions, leading to more efficient maintenance operations, and decrease operational costs, and preventing unplanned downtime.
Through monitoring, the data of the production facilities are linked to the time of occurrence and thus investigations can be carried out as the first measures. This analysis and linking of further asset data, in the context of digital transformation in maintenance, also from the past and from different sources in the company, is a small, manageable first step towards a condition-based maintenance monitoring system and well-maintained equipment.
From Digital Maintenance to Condition Based Monitoring
As soon as the message is available in the system as described above, the evaluation of the machine data can be started immediately.
By analyzing the available asset data and linking the data with the actual state of the machine directly during the malfunction (timestamp), conclusions can be drawn about the exact root cause of the malfunction and measures can be initiated to restore the target state.
The integration of AI-based predictive maintenance into digital maintenance strategies paves the way for condition-based monitoring systems. By leveraging AI algorithms to analyze historical data and real-time equipment performance, companies gain significant benefits such as a comprehensive understanding of machinery, asset health, and performance issues. Predictive insights enable them to identify potential issues before they escalate, allowing for targeted maintenance efforts and minimizing production disruptions. This shift from reactive to proactive maintenance results in improved productivity and optimized resource allocation.
Through this analysis, values can be set per plant that define the target state of a specific plant component at which the production plants run without quality losses. Furthermore, it can be analyzed when downtime is likely to occur in the future. If further factors are considered and simple Lear algorithms from e.g., Python are applied, the prediction for the time of equipment failures can be regularly optimized.
By finding a dedicated solution to a problem using regressions and correlations, the first, simple step into predictive maintenance is successfully taken.
It is important to have a real understanding of processes and data for this scenario. This is because there is not a lot of data from which the appropriate correlations have to be found and the correlations for predictive maintenance, possibly also through the technical intuition of an engineer.
With these prerequisites, as well as the exact identification of the time of the problem, specific predictions can be made about disruptions occurring with a relatively manageable effort and negative effects for the company can be avoided.
Embrace the Power of Digital Maintenance
Digital Maintenance is not only important for a simple maintenance process. It is the basis for a condition-based monitoring system.
Digital maintenance simplifies the maintenance process and provides more transparency with up-to-date information. With this maintenance strategy, companies can significantly improve their plant availability and avoid downtime.
Through the automatic link with the ERP, the recording of maintenance costs and inventory become part of the initiative, benefiting asset-intensive industries.
By linking the machine data, a condition-based monitoring system is ultimately achieved.
Digital maintenance, empowered by AI-based industrial analytics, is revolutionizing the way industries approach maintenance and reliability functions. By embracing AI-driven data collection, analysis, and predictive insights, companies can elevate their maintenance practices, enhance equipment reliability, and maximize overall productivity with Digital maintenance simplifies the maintenance process and provides more transparency by up-to-date information.
Digitalization combined with predictive maintenance enables businesses to eliminate safety risks through detailed description, optimize maintenance schedules, and reduce operational costs. In this age of technological advancement, embracing digital maintenance and AI-driven solutions is essential for staying competitive advantage and future-ready.
Our Cutting-Edge Industrial Analytics Accelerator with predictive maintenance module
To further enhance your digital maintenance capabilities, we are thrilled to introduce our cutting-edge Industrial Analytics Accelerator for Industry, leveraging the latest AI technologies. This solution is designed to empower your company with advanced AI-based predictive maintenance, anomaly detection, and predictive quality solutions, enabling you to stay at the forefront of implementing digital transformation and productivity in your industry.
Read more about Industrial Analytics Accelerator and watch the demo.
