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Fix It Before It Breaks: IIoT Mineral Processing Plant Solution

Continuous Remote Monitoring of Sites

Through many years of development, ALPHA has applied advanced Internet of Things intelligent technology to mining industry, especially mineral processing plant. The equipment of the coal preparation plant is in order and connected with each other. In order to ensure the stable operation of the equipment, ALPHA provides real-time and online status monitoring for the equipment of coal washing plant through the Industrial Internet of Things (IIoT) sensing technology. Combined with AI big data analysis and intelligent operation and maintenance equipment, it achieves predictive maintenance, so as to improve the intelligent management and efficiency of the coal washing plant.

Fig.1 Watch this picture showing how Alpha is developing IIoT minerals processing plant monitoring

In this system, the front-end IIOT sensors go deep into the factory, communicate with the machinery in secret language, store and upload these key data at the same time, gather in the cloud computing center of ALPHA, assist machine learning and industry experience, and then form a predictive maintenance system with continuous and in-depth circulation. The main goal of the system is to carry out the life cycle health management of the equipment, so that the equipment can always operate in an excellent state, eliminate the high wear caused by fatigue operation, and reduce or even eliminate the unexpected shutdown events caused by equipment failure, so as to improve the production efficiency and productivity return of the plant.

Fig.2 Overview Interface of the PdM system of IIoT mineral processing plant

IIOT Predictive Maintenance

Step 1: Data Collection

The first level of ALPHA IIOT Predictive Maintenance System is data collection. Through the AD005 Wireless Vibration & Temperature Sensor (wireless) independently developed by angel method, it is installed on the mechanical rotating equipment to measure the running state of the equipment in real time, then display the acceleration single peak value, kurtosis, effective value of speed (intensity), temperature, battery power, etc. of the vibration in two directions. Data collection and processing, stable real-time transmission to the lower level, repeater.

Fig.3 AD005 Rainbow sensors

Step 2: Data Transmission

The second level of ALPHA IIOT Predictive Maintenance System is data transmission. The signal received by the sensor is transmitted wirelessly to the AJ001 Wireless Repeater which is also independently developed by ALPHA. The repeater, through the connection of cable and optical fiber, stably transmits the optical signal data loaded with measurement data to the local data storage through photoelectric conversion.

Fig.4 AJ001 Wireless Repeater

Step 3: Cloud Data

The local data is uploaded to ALPHA Cloud through the network. The data received by each sensor is displayed in real time, and the operation data of each device is viewed online. And also stored in the cloud, historical data can be viewed at any time.

Fig.5 ALPHA Cloud

Step 4: Data Analysis and Early Warning

The AI intelligent algorithm embedded in ALPHA Cloud can learn the operation of each device at any time, and the AI machine learning algorithm and model can analyze and evaluate the health status of the monitoring device. Through AI machine learning, it can automatically set warning and alarm thresholds for each device, and intelligently predict the failure time and maintenance suggestions by continuously sensing the subtle abnormal changes of the device. Through the increase of big data, more and more accurate prediction of equipment failure, to achieve more intelligent operation and maintenance.

（Here's an AD: Alpha has launched its latest Fault Analysis Service, which is based on Alpha's advanced algorithms and extensive equipment failure models to provide algorithms and analysis services for sensors already in place in coal preparation plants. The point is that the fee is very low. If you are interested, please pay attention to the contact information at the end of this article.）

Fig.6 Data Analysis and Early Warning

Step 5:Human Computer Interaction

Considering the user experience, ALPHA processes the early warning information of various devices by binding Wechat and mobile phones of relevant personnel. Push alert information and predictive maintenance suggestions according to different position levels. No real-time observation of the computer, no missing information. Through the hierarchical management, the mobile phone confirms the early warning information, Misreports or approves the maintenance plan, which is convenient and fast.

Fig.8 Human Computer Interaction

Remote support

It's not just the learning of intelligent system automatically for equipment operation changes, but also the accumulation of big data. And the operation and maintenance opinions of industry experts are a rare wealth. ALPHA has an expert team with more than 30 years of on-site experience. It also remotely monitors the operation of the mineral processing plant equipment through the ALPHA cloud system. According to the feedback of the data information of the equipment, combined with many years of on-site opinions, it approves and gives the operation and maintenance opinions. And through long-term communication with the relevant technical personnel on site, the operation and maintenance opinions will be more and more combined with the on-site experience, while the AI machine through long-term learning of expert experience, intelligent solutions are more and more suitable for each on-site situation. Two experts are embedded in the system, one is AI data intelligence expert, the other is equipment maintenance expert.