Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks.

The task of ore transportation is performed in all mines, regardless of their type (open pit/underground) or mining process. A substantial number of enterprises utilize wheeled machines to perform ore haulage, especially haul trucks and loaders. These machines' work consists of repeating cycles, and each cycle can be divided into 4 operations: loading, driving with full box/bucket, unloading and driving with empty box/bucket. Monitoring this process is essential to create analytical tools that support foremen and other management crew in achieving effective and optimal production and planning activities. Unfortunately, information gathered regarding the process is frequently based on operators' oral testimony. This process not only allows for abuse but is also a repetitive and tedious task that must be performed by foremen. The time and attention of foremen is valuable as they are responsible for managing practically everything in their current mine section (machines, operators, works, repairs, emergencies, safety, etc.). Therefore, the automatization of the described process of information gathering should be performed. In this article, we present two neural network models (one for haul trucks and one for loaders) build for detecting work cycles of the ore haulage process. Both models were built utilizing a 2-stage approach. In the first stage, the models' structures were optimized, while the second was focused on optimizing hyperparameters for the structure with best performance. Both of the proposed models were trained using data collected from on-board monitoring systems over hundreds of the machines' work hours and utilized the same input features: vehicle speed, fuel consumption, selected gear and engine rotational speed. Models have been subjected to comprehensive testing during which the efficiency and stability of the model responsible for haul trucks was proven. Results for loaders were not as high quality for haul trucks; however, some interesting facts were discovered that indicate possible directions for future development.

Application of Spectral Entropy in Haul Truck Joint Damage Detection.

Early detection of machine failures is often beneficial, both financially and in terms of worker safety. The article presents the problem of frequently damaged joints in haul trucks, which are a real threat to the health and life of drivers. It was decided to investigate the problem in terms of dynamic overloads using two NGIMU inertial sensors and placing them in two places on the machine in close proximity to a joint. The data were captured during the standard operation of various machines in several mining departments, which allowed for the detection of a variety of factors influencing vibration. A hypothesis was developed that any changes in the joint would cause a change in the characteristics of vibrations, which were measured using the spectral entropy of vertical vibrations. Analyses have shown that there is a relationship between the change in spectral entropy difference (between the front and back of the vehicle) and joint events: nut tightening, nut replacement, and even joint fracture and replacement. The presented results offer the potential to create a tool for joint diagnostics and the early detection of damage or backlash.

Application of Wearable Computer and ASR Technology in an Underground Mine to Support Mine Supervision of the Heavy Machinery Chamber.

Systems that use automatic speech recognition in industry are becoming more and more popular. They bring benefits especially in cases when the user's hands are often busy or the environment does not allow the use of a keyboard. However, the accuracy of algorithms is still a big challenge. The article describes the attempt to use ASR in the underground mining industry as an improvement in the records of work in the heavy machinery chamber by a foreman. Particular attention was paid to the factors that in this case will have a negative impact on speech recognition: the influence of the environment, specialized mining vocabulary, and the learning curve. First, the foreman's workflow and documentation were recognized. This allowed for the selection of functionalities that should be included in the application. A dictionary of specialized mining vocabulary and a source database were developed which, in combination with the string matching algorithms, aim to improve correct speech recognition. Text mining analysis, machine learning methods were used to create functionalities that provide assistance in registering information. Finally, the prototype of the application was tested in the mining environment and the accuracy of the results were presented.