The following is the The winning cornerstone of digital operation and maintenance From Predictive maintenance recommended by recordtrend.com. And this article belongs to the classification: research report, Roland Berger .
With the innovation of Internet technology, the application and accumulation of big data, the improvement of computing power and the rapid development of related model theory, the application scenarios of artificial intelligence are gradually enriched, and in recent years, it is gradually transiting to practical operation. All walks of life are actively exploring the operation mode enabled by artificial intelligence to promote industrial upgrading and long-term operation transformation.
On the policy side, the provinces have successively introduced the support policies for the intelligent manufacturing industry, which gradually gave birth to the digital and intelligent transformation of the manufacturing industry. Operation and maintenance services (such as spare parts sales, equipment maintenance and repair, etc.) as an important component of the manufacturing industry, create innovative application scenarios through the deep integration of industrial Internet and artificial intelligence, and achieve the trend of continuous cost reduction and efficiency.
From the perspective of demand side, complete digital operation and maintenance services will become one of the main dimensions for enterprises to choose operation and maintenance service providers. Roland Berger’s survey on the world’s leading manufacturing enterprises shows that more than 85% of the respondents think that the operation and maintenance service providers should pay more attention to the digital scheme planning ability, and 97% of the respondents think that the operation and maintenance service providers should provide active and complete digital operation and maintenance service schemes. With the development of advanced manufacturing industry, the dimension of operation and maintenance service has changed. The traditional concept of spare parts purchase and regular maintenance is out of date. The digital operation and maintenance solutions enabled by industrial Internet and artificial intelligence will be a new trend.
What is predictive maintenance? Why do we need it?
The development process of operation and maintenance services is mainly divided into four stages
This is the most original way of operation and maintenance service, which usually refers to the arrangement of technicians to repair after mechanical failure. Because this maintenance mode usually occurs after the equipment failure, it is highly unpredictable and sudden, and the equipment itself has a high degree of damage, which is easy to cause high repair time and cost, and also easy to cause side effects such as high shutdown time and cost.
Condition based maintenance
In addition to providing a proactive monitoring mechanism for equipment maintenance, predictive maintenance has four advantages, which should be used as the starting point of digital operation and maintenance solutions
Advantage 1: little impact on the production line – unlike intelligent manufacturing, which needs to upgrade the production line itself, predictive maintenance hardware mainly helps to establish the connection between the equipment and the server. It does not need to change the production line or production process, and has little impact on the overall production scheduling.
Advantage 2: high replicability – the solution can be quickly copied on the same device, and more devices can bring more massive data, which is more helpful to improve the accuracy of the model.
Advantage 3: Substantial Results—— According to Roland Berger’s project experience, predictive maintenance can reduce MRO (maintenance, repair, operation) cost by 5-10% and overall maintenance cost by 5-10%; while in terms of efficiency improvement, predictive maintenance can improve equipment normal operation time by 10-20%, reduce equipment maintenance time by 20-50%, and provide better service for product quality Security.
Advantage 4: extensive application scenarios – the main principles of predictive maintenance are based on equipment networking, data acquisition, big data analysis and machine learning. It has great development potential in the future, and gradually promotes the application scenarios to extend from equipment maintenance to scheduling and asset management. The following will introduce this.
Roland Berger predictive maintenance solution and implementation practice
The key elements of Roland Berger’s digital operation and maintenance solutions are as follows: from the design and implementation of the project to the prediction of the key elements
Hardware: mainly responsible for local data acquisition and analysis, covering data acquisition equipment (sensor + data transmission equipment) and edge computing server:
Software: mainly used for fault detection model building and cloud storage and processing
Based on the predictive maintenance architecture, we also need to carry out targeted opportunity diagnosis, data collection, algorithm construction and landing verification for customer equipment. The key steps are as follows:
Introduction of opportunity analysis: there are many key parts of production equipment, but if all of them are imported into predictive maintenance, it may cause unnecessary cost waste. It is suggested to consider according to the actual needs. Roland Berger’s methodology can help customers identify the most suitable introduction opportunities. Taking the application case of a certain brand of CNC lathe as an example, we cross measure the key dimensions such as component cost, fault frequency and fault influence scope. Finally, we suggest that customers start with the tool and spindle, and strive to achieve the best application benefit with the minimum cost.
Machine learning model building: machine learning model is the core of predictive maintenance solution. For the introduction of predictive maintenance, we have developed a set of complete and empirical effective model building method, which can effectively enable fault prediction. The following cases are illustrated by the tool fault prediction algorithm of a brand of CNC lathe
Practical application: through the tool wear prediction model, we have successfully assisted customers in effective fault early warning and detection. At the same time, based on customer demand, the fault prediction system can also make fault alarm or real-time adjustment of production scheduling through the connection with the production scheduling system.
Digital operation and maintenance scenario based on predictive maintenance
Through the accumulation of project experience and the long-term and extensive discussion of internal and external experts, Roland Berger believes that predictive maintenance is the key step of digital operation and maintenance. Through the software and hardware enabling of predictive maintenance, there are a wide range of subsequent application scenarios, such as:
Digital asset inventory: it can collect data from sensors on equipment through predictive maintenance management platform, and confirm the existence of physical assets by comparing with asset account, so as to effectively reduce the waste of human resources in inventory.
Digital maintenance operation and maintenance: the real-time processing data detected by predictive maintenance can become an important input of the overall operation and maintenance management system of the factory, and the subsequent maintenance service process and spare parts material pulling process can be started based on the data analysis results.
Production and material control system enabling: Based on the sensor installed on the equipment to transmit data and automatic control system data, the production and material control system can collect, store and organize processing data in real time, and form a visual report, which is an important input of production management.
Supply chain enabling: the equipment fault prediction system can compare the quality performance of key components from different sources, which is an important basis for supplier quality management. At the same time, purchasing can use relevant data for supplier performance evaluation and as the basis of bargaining.
Predictive maintenance is an important realization of artificial intelligence in the field of digital operation and maintenance. It has the characteristics of initiative and intelligent learning, and can help to achieve higher output quality, less temporary shutdown and lower operation and maintenance cost. It is the cornerstone of digital operation and maintenance, and also the further enrichment of digital intelligent manufacturing.
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