Predictive maintenance brings changes to the mine

Predictive maintenance brings changes to the mine

The mining industry entered the era of 4.0, "Internet + mine" has been destined to become a new model of mining industry development, network, data, informatization has gradually become a new feature of the development of mining enterprises, but also help mining enterprises to achieve efficient, safe, green and sustainable development, the most critical issue in the development process is to innovate the mine management model, establish an information management system, and finally realize the intelligent and efficient management of mining industry production. Integration, industry interconnection and datafication of decision-making.

At present, based on the monitoring of big data and the realization of predictive maintenance, it has become an important breakthrough. In June last year, McKinsey & Company released a report, "Artificial Intelligence: The Next Digital Frontier," which presented How artificial intelligence will lead to a shift from preventive maintenance to predictive maintenance "in the future.". Absolutely this point of view is right, but it is also a fact that this predictive maintenance capability already exists.

As Industry 4.0 continues to advance the development of data science, artificial intelligence technologies that accurately detect and signal when maintenance is needed will evolve. Contrary to McKinsey's report, many companies, such as Chengdu Alpha Intelligent Technology Co., Ltd., are already using data-driven insights to go beyond preventive maintenance. Today's enterprises do not need to implement subjective maintenance service scheduling, and avoid the inevitable waste, redundancy and disruption that comes with it. Maintenance is now carried out in a more dynamic strategy.

Companies often use online data to track performance variables in real time, that is, to monitor machines through a variety of IoT sensors. When these variables indicate that machine performance is declining, technicians can intervene before machine assets fail and need to shut down a production line. Maintenance is carried out only when necessary and at the same time before irreparable. This is not just about mining, but also the ability that maintenance, repair and operations personnel from manufacturing, oil and gas, to pharmaceuticals, to retail, have been dreaming for years. Now, this wait can be over.

Trends in Intelligent, predictive maintenance of industry.

In the mining sector, large equipment is numerous and interconnected. For example, each equipment in a coal preparation plant and a concentrating plant is interconnected for each link in production, and the working relationship of each machine is inseparable. Therefore, the operation protection of the equipment and maintenance have become an important task that cannot be ignored.

But how long can the traditional patrol maintenance method extend the life of the equipment? Can it reduce equipment failure? It is not difficult to see that there are many drawbacks:

1. Equipment is often in an overloaded state of operation (especially when production tasks are heavy), increasing the likelihood of glitch. A glitch is highly susceptible to fatal failures due to its low visibility and difficulty in being monitored by maintenance personnel.

2. Passive maintenance is the processing method of most traditional enterprises. After the equipment has failed, certain measures are taken to repair it. To a certain extent, the damage to the equipment is aggravated and the production efficiency is also unfavorable.

3. The maintenance method relies too much on labor and is inefficient. The company will conduct special inspection and monitoring of the equipment. The limitations and untimeliness of the visual inspection have resulted in poor results. The experience of the experienced staff will be better, but the experience can't be copied, the old and new employees are also a problem.

These common drawbacks have an impact on the equipment itself and production efficiency, and gradually reduce the overall efficiency of the company.

Alpha is committed to intelligence, real-time monitoring of equipment operation through self-developed IoT sensors. The wireless monitoring intelligent early warning system developed by Alpha is to collect data and then process the data through the calculation of the core algorithm. At present, the core of most enterprises is still the same as the algorithm of the system, the same is true for Alpha, and the intelligent AI module is deployed in the system, so the system can self-learn and judge. As a result, another core of the early warning system has shown importance, "big data".

The system not only needs to complete data collection and display, but also process and analyze. The huge amount of "big data" gives the system enough data for reference, and the system is more accurate in judging the abnormal state of the machine. Moreover, the system will adjust and judge the data according to the life cycle and life of each different device. As device parts age, their response to stress is different from the new one. Therefore, maintenance plans should be adjusted over time to account for changing failure rates that can be machined to output new models.

Equipment has different performances at different stages of the life cycle. Equipment failure has a “bathtub curve” that divides equipment life into three main phases: early failure rate phase, steady state phase, and loss phase. Usually the machine will often fail at the beginning of its service life. However, as time goes by, it will enter a stable period, the maintenance process will gradually disappear, and the fault is even rarer. In the later stages, the machine failure rate will rise and eventually be scrapped.

failure rate will rise and eventually

Finally, according to the analysis of the system, each specific device is alerted in an abnormal state. Through the input of processing methods and system learning, more and more accurate reference processing schemes will be given. Considering the user experience, information is directly sent to SMS, and WeChat is pushed to personnel with administrative rights. And the inventory management system that is included in the Alpha system can record materials such as spare parts of the equipment into the system. When the early warning treatment plan is approved, the required spare parts can directly display the storage location number. In the planned shutdown maintenance, prepare in advance, pick up the goods, and maintain the work efficiently and quickly. The inventory is small and the procurement plan is automatically given.

Predictive maintenance brings changes to equipment monitoring

In addition, the installation conditions in the plant area are complex. Alpha develops sensors into miniaturization and integration, integrates sensors such as vibration and temperature, and transmits them through wireless transmission, with stable transmission, strong anti-interference ability, and perfect solution. The complexity of the construction of the peripheral wiring of the equipment. No matter from the pre-installation or post-maintenance replacement, it saves a lot of complicated work and does not affect the normal operation of the original equipment.

Through early warning and monitoring, predictive maintenance is achieved, and the stable and good operation protection of the equipment is the key.

Through early warning and monitoring, predictive maintenance is achieved, and the stable and good operation protection of the equipment is the key

1. Minimize unplanned downtime and avoid sudden failures

2. Reasonable maintenance of equipment according to faults, which can reduce planned downtime and extend equipment life.

3. Minimize the cost of spare parts and consumables, and optimize inventory management of spare parts

4. The workload of on- site inspection personnel greatly reduced

Under the opportunity of Industry 4.0, the aviation, automotive and other industries have maturely applied predictive maintenance to optimize machine management. Mining also needs to apply more cutting-edge intelligence to improve safety and efficiency. Predictive maintenance is not the only direction, but the intelligent management process of industrial equipment must go through. Digital operation and maintenance of data-driven decision-making will become the foundation of the industrial Internet, a real mine management revolution.

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