Solutions for predictive maintenance of machines
Manufacturing companies in Germany are investing in Federal Statistical Office invest over 50 billion euros a year in machinery and equipment. Efforts to prevent breakdowns are correspondingly high. Predictive maintenance is therefore becoming a central component of Industry 4.0.
Production chains are becoming increasingly complex. This means that they are affected by defects much more frequently and to a greater extent. An outdated maintenance strategy can reduce a site's production capacity by up to 20 percent. More and more companies are therefore turning to predictive maintenance. In contrast to preventive maintenance, predictive maintenance relies on the prediction of maintenance requirements for machines and systems in order to minimize downtimes and reduce maintenance costs. Sensor data is used for this purpose, which many machines are already equipped with at the factory.
Preventive maintenance means carrying out maintenance measures according to predefined criteria, for example changing a light bulb every 7,000 operating hours. Predictive maintenance, on the other hand, means that the maintenance effort is automatically determined on the basis of key figures and the relationships between these key figures and optimized for low downtimes and high machine effectiveness. Predictive maintenance can increase the availability of production systems by 10 to 20 percent compared to preventive maintenance, while reducing maintenance costs by 5 to 10 percent.
Example Rolls Royce - Extended time between maintenance by up to 50
The engine manufacturer optimizes the maintenance intervals for each engine individually and can increase the time between two maintenance intervals for an engine by up to 50 %. For other engines, the need for maintenance can also be identified earlier. This leads to an immense reduction in the stock of spare parts and an increase in the efficiency of engines with overdue maintenance requirements. The motors are monitored while they are in use. If maintenance is required, maintenance options are automatically checked and appropriate measures are planned. Expensive and unplanned breakdowns are thus avoided.
How does predictive maintenance work?
In short, predictive maintenance in Industry 4.0 means that the maintenance requirements of systems and machines are determined automatically and individually. In doing so, an algorithm continually approaches the optimum time for maintenance in order to minimize downtimes and save costs. The optimum interval between two maintenance tasks can vary greatly between machines of the same model, as each machine is analyzed individually.
For predictive maintenance, an algorithm learns certain patterns from data that indicate a need for maintenance. Sensor data is used in almost every solution, but more and more systems are using additional image recognition or external information such as the type of material used. For example, fatigue in the material can be detected by a change in sound. Sensors then detect sound frequencies that are inaudible to humans. This allows signs of an impending defect to be detected up to two weeks earlier.
Collection of sensor data
Die <a href="https://taod.de/services/bi-und-data-analytics-consulting“ data-webtrackingID="blog_content_link" > Datenanalyse </a> erfolgt über Sensordaten. Bereits ein Drittel aller genutzten Maschinen in Deutschland sind mit Sensoren ausgestattet. Gerade neue Modelle erhalten immer häufiger integrierte Sensoren. Daten wie Temperatur, Betriebsdauer, Drehzahlen, Druck und Vibration werden für die vorausschauende Wartung besonders häufig benutzt.
Grundsätzlich gilt: Mit großen Datenmengen wird die Bestimmung von Wartungsaufwänden deutlich zuverlässiger. Viele mittelständische Unternehmen, die bisher nur wenige Berührungspunkte zu Big Data und <a href="https://www.taod.de/services/artificial-intelligence-consulting“ data-webtrackingID="blog_content_link" > künstlicher Intelligenz </a> hatten, klagen über Hürden in der Implementierung, verfügen nicht über die nötigen Kompetenzen im Unternehmen und sehen hohe Kosten auf sich zukommen. Doch diesen Herausforderungen der Industrie 4.0 lässt es sich mit der richtigen Langzeitstrategie kompetent begegnen.
Example ALCOA - 20 % reduction in machine downtimes
The American aluminum manufacturer is implementing a proof of concept with 50 machines in order to increase the level of service and save maintenance costs. The 50 machines are equipped with sensors that measure the current in the machine, among other things. For example, an unexpected increase in amperage is detected, indicating a faulty motor. The source of the fault, a damaged belt, is quickly found and rectified, so that twelve hours of unplanned downtime can be avoided. By implementing the PoC, potential downtimes of the selected machines are reduced by around 20 %. The investment pays for itself within the first six months.
Challenges and solutions for initial projects with predictive maintenance
Every company operates under individual circumstances, framework conditions, requirements and objectives. However, there are also a number of challenges that are regularly encountered in predictive maintenance projects. These challenges can be categorized and then systematically solved.
Data acquisition
Data forms the basis for every learning algorithm. But first of all, it has to be collected. Data acquisition and data management work when data is collected, transmitted and stored. More and more systems have integrated sensors that collect data automatically.
Solution for data acquisition
Die Datenerfassung ist eine Zusammensetzung aus mehreren Teilprozessen. Jeder dieser Teilprozesse sollte den Best-Practice-Standards unterliegen. Diese Standards bieten zwei Vorteile: Wiederverwendbarkeit und Robustheit. Funktionierende Teilschritte können mit leichten Anpassungen für andere Fälle verwendet werden. Dadurch sinkt der Aufwand für den Aufbau und die Weiterentwicklung von Systemen zur Datenerfassung und diese bleiben trotzdem flexibel. Gleichzeitig wird ein hoher Qualitätsstandard sichergestellt. Die einzelnen Komponenten sind vielfach praxiserprobt und so optimiert, dass sich der schnelle Aufbau des Systems mit hoher Leistungsfähigkeit vereinen lässt. Die Auswahl der konkreten Werkzeuge ist dabei von größter Relevanz. Erfahrung mit <a href="https://taod.de/tech“ data-webtrackingID="blog_content_link" > geeigneten Technologien </a> kann den Aufbau der Datenerfassung immens beschleunigen und langfristige Kosten auf ein Minimum reduzieren.
Data preparation
The entire data capture and all sub-steps are optimized to enable a smooth and fast process. Data is packaged and compressed for this purpose. This allows data to be used very flexibly while keeping storage and transfer costs low. However, for an algorithm to learn from data, it must be converted into a different format. In addition, data can always be incorrect or incomplete. High data quality is a prerequisite for reliable artificial intelligence. The so-called "Rule of Ten" is one of the most important rules when working with data. It states that work steps with high-quality data are 90% cheaper than with incorrect data because they are completed much faster and produce far fewer errors.
Solution for data preparation
Choosing the right tools is also crucial for ensuring high data quality. The development of effective processes for controlling data quality is crucial here. An effective control process provides insights into which quality deficiencies exist and how these can be rectified.
Der Einsatz von <a href="https://www.taod.de/services/data-engineering-consulting“ data-webtrackingID="blog_content_link" > Engineering </a> Best Practices sorgt dafür, dass die Datenqualität auf einem hohen Level gehalten werden kann. Tools wie <a href="https://www.taod.de/tech-beratung/dbt-labs“ data-webtrackingID="blog_content_link" > dbt </a> machen die Umsetzung von Best Practices in der Datenaufbereitung auch für junge, kleine Data-Teams möglich. Richtig konfiguriert sorgt dbt dafür, dass Mängel erkannt werden, schon bevor sie tatsächlich auftreten.
Data combination
Artificial intelligence is based on recognizing rules and patterns in data. In order for this to happen, these patterns must be contained in the data. One of the most important success factors for predictive maintenance projects is the enrichment of data. Certain regularities can only be uncovered by adding further information.
Solution for data combination
At first glance, combining data from different source systems may seem like an additional expense. In fact, this step can actually reduce the costs of predictive maintenance and provide additional flexibility.
In order to identify meaningful possibilities for data enrichment, technical experience in the use of systems and machines is of the utmost importance. Through the exchange between AI developers and production employees, possible correlations are exchanged directly and can also be integrated into the predictive maintenance system in the shortest possible time. All information used is assessed according to its added value by comparing the improvements in performance and effort. This comparison takes place automatically and is permanently available for further optimization and controlling.
Automotive manufacturing example 30 % increase in machine uptime
A car manufacturer increases the uptime of its machines by 30 % within 24 months of the start of the project. 30 %. Temporarily installed sensors are used, which only need a few minutes to scan each machine. The investment in sensor equipment therefore remains low. The sensor data used includes vibration, infrared temperature measurement, ultrasound and electrical voltage.
Success factors for projects with predictive maintenance
Der Erfolg von Projekten mit Predictive Maintenance ist durchaus vorbestimmbar, wenn wichtige Grundvoraussetzungen erfüllt sind. Im Bereich der <a href="https://taod.de/services/bi-und-data-analytics-consulting/“ data-webtrackingID="blog_content_link" > klassischen Datenanalyse </a> müssen Unternehmen deshalb dringend ihre Hausaufgaben machen. Denn eine Faustregel gilt es ganz dringend auch in der Industrie 4.0 zu beachten: Erst Data Analytics, dann Data Science.
Transparency of the maintenance effort
Selbst wenn eine ausreichende Datenbasis vorhanden ist, bedeutet das nicht, dass diese auch gesehen werden. Damit die Daten nicht nur auf dem Server oder in der Cloud gesammelt, sondern auch sichtbar werden, müssen sie visualisiert und allen Verantwortlichen zugänglich gemacht werden. Die Umsetzung kann über Dashboards, automatisierte Mails sowie die Integration in bestehende Kommunikationswerkzeuge wie Slack oder Trello erfolgen. Die Arbeit eines Produktionsleitenden oder technischen Asset Managers wird mit diesen, oft in Echtzeit ausgelieferten Daten, zuverlässig ergänzt. <a href="https://taod.de/services/bi-und-data-analytics-consulting/“ data-webtrackingID="blog_content_link" > Professionelle Datenanalyse </a> über einen Modern Data Stack gilt deshalb als Erfolgsgarant für Predictive Maintenance.
Prediction of the maintenance effort
When and how much effort is required to maintain machines can be predicted with the help of artificial intelligence and the use of corresponding machine learning algorithms. Algorithms make machines capable of learning. It is important that the measured data is monitored smoothly in order to create system diagnoses, automatically detect deviations and suspected problems and provide a forecast of the usable remaining service life. Which AI algorithm is used depends on the machine being monitored. A distinction is made between classification and anomaly detection. While classification is a sensible option for monitoring machines with a high failure rate, anomaly detection is used for devices that are hardly susceptible to faults. Monitoring them is much more complex, as there are rarely any signs of failure.
Raw data from sensors
For predictive maintenance, sensors are used that are already integrated in the system or can be installed with little effort. A distinction is made between necessary and additional data. Necessary data is a prerequisite for predictive maintenance. Additional data improves the quality of the predictions, but is not a must. The most important data to be collected is information about past failures, temperature, operating time, speeds, pressure, vibration and indirect parameters such as the material used or suppliers. Which of these are necessary is decided on a case-by-case basis.
Example Global Mining Company Reduction of transportation failures by 50 % through anomaly detection
The mining products company needs around 10,000 wagons to transport materials. Each breakdown of a wagon costs at least 6,000 US dollars. Originally, around 2,000 unplanned breakdowns were recorded each year. The aim of the project is to reduce downtime and repair costs using only data that has already been collected. Integrated sensors are already being used so that operational processes are not impaired by the collection of data such as temperature, sound and image recognition. With predictive maintenance and anomaly detection, failures are reduced to less than 1,000 per year using an anomaly detection algorithm.
Process of a predictive maintenance project
A predictive maintenance project requires careful preparation, but can then be quickly put on track. Basically, it can be divided into three phases: concept, MVP and analysis:
.webp)
