Recommended by cloud maintenance experts: Digital operation and maintenance and predictive maintenance are an important part of safe production in factories

Recently, fires and other safety accidents have occurred one after another in some places, exposing outstanding problems such as inadequate investigation and rectification of hidden dangers and failure to implement safety production responsibilities, which has once again sounded the alarm. Production safety includes the safety of the production environment, equipment, etc. It is of great significance to strengthen risk monitoring of the production environment and equipment operation monitoring to prevent casualties and equipment accidents, and to achieve accurate early warning of risks and prediction of abnormal production equipment. As an important participant in safety production, security product technology will increasingly play an irreplaceable role. Through artificial intelligence, cloud computing, Internet of Things, big data, AI vision and other technologies, we provide safety production monitoring and equipment predictive maintenance solutions for industrial production scenarios. Digital operation and maintenance and predictive maintenance are an important part of safe production in factories of manufacturing enterprises. This article will analyze and discuss relevant paths from the perspectives of abnormal prediction, operation and maintenance of production equipment in the factory workshops of manufacturing enterprises.

1. The value of digital operation and maintenance

With the development of industry and science and technology, an overall trend in the development of modern equipment is to develop in the direction of density, complexity, intelligence and automation. It is not uncommon for malfunctions in the operation of in-service equipment to cause vicious accidents. Modern industry urgently needs to adopt the method of ensuring the safety of equipment during operation. Monitoring technology related to the safe operation of in-service equipment can reveal the development and evolution rules of equipment operation status, achieve early failure prediction, and thereby avoid failures, especially the occurrence of malignant production safety accidents.

The traditional "hands-on-shoulder" operation and maintenance model relies entirely on manual technical skills, resulting in low efficiency in operation and maintenance management, and has limitations and hidden dangers in the following four aspects:

First, it is difficult. The traditional method of manual fault inspection and diagnosis requires high technical level of analysts and makes maintenance difficult.

Second, the risk of manual inspection is high. Traditional equipment monitoring relies on personnel to enter dangerous workplaces and equipment for inspection and testing, which is highly risky and greatly reduces efficiency.

Third, it is difficult to control the real-time status of production equipment. Without accurate sensors to collect data, it is difficult to understand the status of equipment in real time and perform precise analysis, and it is impossible to effectively control the real-time status of production equipment.

Fourth, failure cannot be predicted. Without accurate data and analysis support, it is impossible to predict equipment failure, which will greatly increase the risk of equipment failure and reduce production efficiency.

In order to solve these problems, the status monitoring of production equipment by factories and enterprises has gradually shifted from manual inspection to real-time monitoring with digital intelligence. Digital tools are used to solve the predictive maintenance needs of production equipment and effectively solve the factory management and production problems of manufacturing enterprises. Equipment maintenance. Operation and maintenance has always been an indispensable part of the production and operation management of manufacturing companies. Using the new generation of information technology to change the operation and maintenance model can specifically bring the following value to manufacturing companies' factory production:

The first is to ensure continuous operation. Based on reliable and real production equipment operation data, the current operating status of the equipment can be judged and the future status can be predicted. It can accurately locate faulty components, analyze the root cause of the fault, comprehensively monitor the fault degradation trend, evaluate the remaining life of the faulty component, and make maintenance and repair decisions. The transformation from temporary and after-the-fact emergency repairs to planned and predictive maintenance can effectively reduce the number of unplanned downtimes, thereby ensuring and effectively improving the comprehensive benefits such as the enterprise's production equipment utilization rate.

The second is to ensure equipment safety. By judging and predicting the status of equipment, we can avoid chain reactions caused by equipment failures and minimize the risk of safety accidents.

The third is to ensure corporate benefits. In the actual operation and maintenance of an enterprise's equipment, "over-repair" leads to a large inventory of spare parts, and "under-repair" leads to emergency repairs afterwards. Using digital operation and maintenance, the status of the equipment can be grasped in real time, predictive maintenance can be achieved, and "over-repair" can be minimized. Or "under repair", optimize the spare parts inventory, reduce the occupation of spare parts funds, and reduce the post-repair ratio.

The fourth is to establish a fault database. With the accumulation of large amounts of historical data and process data, the establishment of an equipment fault case library with the company's own characteristics and the enrichment of typical fault models can continuously promote the application of fault modeling intelligent analysis technology in the enterprise, making the analysis conclusions more intelligent and accurate.

The fifth is to help upgrade the operation and maintenance management model. Based on reliable and real equipment operation data, it can realize the judgment of the current operating status of the equipment and the prediction of the future status, change the previous equipment operation and maintenance model of post-event maintenance and planned maintenance based on experience, and gradually move towards high-level equipment operation and maintenance based on the equipment status. The management model changes to realize digitalization and informatization drive, transform the equipment operation and maintenance management model, and enter the predictive maintenance model.

2. The necessity of predictive maintenance

The production equipment of industrial enterprises is the material basis for the survival and development of the enterprise, and it is also an important fixed asset of the enterprise. The key to the enterprise's production efficiency and whether the benefits are steadily improved is whether the production equipment can operate normally and safely. To this end, industrial enterprises must strengthen equipment management and perform predictive maintenance.

Simply put, predictive maintenance is a technology that can predict future failure points of machine parts. Through this technology, plans can be made to replace machine parts before they fail. This not only minimizes equipment downtime and maximizes the life of components, but also ensures the stability of production efficiency. Complete predictive maintenance mainly includes three stages: data collection and processing, health monitoring, maintenance management and execution.

  1. Data collection and processing stage

Through the Internet of Things sensor equipment, the characteristic data of the equipment is collected and classified at the same time, providing a data basis for predicting the health status of the equipment.

  2. Health monitoring stage

In this stage, a prediction model needs to be established based on the mechanism or data, and then the collected and classified equipment characteristic data is input into the prediction model, so that the status of the equipment and future change trends can be judged, and the possibility of failure can be predicted in advance. Trends and future equipment health to avoid the hidden dangers of sudden equipment failure and ensure non-stop production lines and production safety.

  3. Execution and management phase of maintenance

In this stage, it is necessary to combine the results of health analysis with the execution management of factory and enterprise equipment, formulate maintenance strategies, monitor the execution of maintenance strategies, record the implementation process of maintenance, and continuously update and iterate through the accumulation of maintenance management data. Maintenance strategy.

There are three ways to maintain equipment: post-event maintenance, preventive maintenance and predictive maintenance:

Post-event maintenance is to take measures to maintain the equipment after it fails. Equipment downtime and work-in-process losses are very large, and it is a costly maintenance method.

Preventive maintenance belongs to advance maintenance, which is a planned and untargeted maintenance of equipment based on time, performance and other conditions.

Compared with post-event maintenance and preventive maintenance, predictive maintenance predicts equipment failures and maintenance needs through real-time monitoring and analysis of equipment operating data, and has the characteristic of performing maintenance as needed.

The development trend of equipment maintenance methods has ranged from post-control to preventive maintenance and then to predictive maintenance. Obviously, predictive maintenance has a broader development prospect.

3. Practical exploration of digital operation and maintenance and predictive maintenance

The core value of operation and maintenance is prevention, from monitoring to alarming, from prediction to maintenance, to achieve full life cycle management of equipment. Industry companies have launched relevant business products, technologies, and solutions to provide manufacturing customers with factory production equipment. Online vibration monitoring, fault prediction, fault diagnosis, intelligent operation and maintenance.