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Initially, Artificial Intelligence (AI) focused on diagnostics during the 70s and 80s. Unfortunately, it did not gain trust and few industries embraced it, mostly due to the extensive manual programming effort that AI required for interpreting data and act. In addition, the computer capacity, for handling the amounts of data necessary to train AI, was lacking the disc dimensions we are used to today, which made it go slowly. Not until the 2000 s con-fidence in AI was established in parallel with the introduction of new tools that was paving the way for PLS, PCA, ANN and soft sensors. Year 2011, IBM Watson (an AI application) was developed and won over the jeopardy champion. Today's machine learning (ML) such as "deep learning" and artificial neural networks (ANN) have created interesting use cases. AI has therefore regained confidence and industries are beginning to embrace where they see appropriate uses. Simultaneously, Internet of Things (IoT) tools have been introduced and made it possible to develop new capabilities such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). These technologies are maturing and could be used in several application areas for the industries and form part of their digitalization journey. Furthermore, it is not only the industries that could benefit from introducing these technologies. Studies also show several areas and use cases where augmented reality has a positive impact, such as on students' learning ability. Yet few teachers know or use this technology. This paper evaluates and analyze AR, remote assistance tool for industrial purposes. The potential of the tool is discussed for frequent maintenance cases in the mining industry. Further on, if we look into the future, it is not surprising if we will be able to see that today's concepts of reality tools have evolved to become smarter by being trained by multimedia recognition and from people who have thus created an AI expert. Where the AI expert will support customers and be able to solve simple errors but also those that occur rarely and thus be a natural part of the solution for future completely autonomous processes for the industry. The article demonstrates a framework for creating smarter tools by combining AR, ML and AI and forms part of the basis creating the smarter industry of the future. Natural Language Processing (NLP) toolbox has been utilized to train and test an AI expert to give suitable resolutions to a specific maintenance request. The motivation for AR is the possible energy savings and reduction of CO2 emissions in the maintenance field for all business trips that can be avoided. At the same time saving money for the industries and expert manhours that are spent on traveling and finally enhancing the productivity for the industries. Tests cases have verified that with AR, the resolution time could be significantly reduced, minimizing production stoppages by more than 50% of the time, which ultimately has a positive effect on a country's GDP. How much energy can be saved is predicted by the fact that 50% of all the world's business flights are replaced by one of the reality concepts and are estimated to amount to at least 50 Mton CO2 per year. This figure is probably slightly higher as business trips also take place by other means of transport such as trains, buses, and cars. With today's volatile employees changing jobs more frequently, industry experts are becoming fewer and fewer. Since new employee stays for a maximum of 3-5 years per workplace, they will not stay long enough to become experts. Introducing an AI expert trained by today's experts, there is a chance that this knowledge can be maintained.

A drone (UAV, unmanned aerial vehicle) is no longer atoy, it is gaining bigger and bigger terrain in the industryas an everyday working tool. Equipped with sensors,thermal cameras, components and system software itmost likely will be part of the solutions that continuesstreamlining the mining operations in the future. Datagathering of signals from sensors mounted on dronesand other mining equipment such as mining vehiclescreates conditions for monitoring, analysis and warningof possible risks and suggests how these can be avoidedin due time. The experimental drone study conducted atan open pit mine Aitik, Boliden (Figure 1) inSweden will be presented in this paper. Aitik is todaythe world's most efficient copper (Cu) open pit mine.The authors propose decision trees to support and enablethe transformation into a completely autonomousmining operation. In combination with deep learning(DL), pattern recognition and artificial intelligence (AI)applications, creates the puzzle pieces to support miningoperations to further increase their productivity andsafety.

A key role for which some sensors have been developed is to be used in many applications that can belife-critical such as with fire. This article is about a number of experimental studies aimed at exploringand identifying sensors opportunities to detect gases emitting on a mining vehicle prior a fire is a fact.The conducted tests showed that some sensors have the potential to detect e.g. oil mist caused bybroken hydraulic oil hoses and other hydrocarbons emitting on a mining vehicle before a fire is a fact.Though with some challenges relating to the distance between the sensor and emitting source.Another challenges has to do with data gathering. Digitization and online monitoring of data beinggathered 24/7 has given rise to several opportunities for the mining industry as for example onlinesupervision of the daily production but also challenges like increased number of networked users anddemand for real-time communication and requirements on minimal latency. Minimal latency is also aprerequisite succeeding with switching to a fully autonomous operation. The mining operations can bedescribed as a hazard and dirty environment with a deep mining shaft that makes wirelesscommunication difficult. With a fully autonomous mining operation exceptional signals and datacollection for planning, monitoring and controlling requires new ideas in order to minimize the risksin such operation. Part of that solution may be to gather data from several different types of sensorsplaced in the mining processes. Sending the sensor data to an overall system with predefined warningand alarm setpoints enables the possibilities with early alarms that allow production personnel to reactbefore it becomes too late. The article concludes by discussing possible diagnostics and decision-making solutions to supports rescue efforts depending on the scenarios that may arise to be includedin an artificial application.

Sensors play a key role today and have been developedto be used in many applications that can be life critical aswith e.g. fire alarms. When mines now start investingin information systems and information technologyinfrastructure, they have taken one step closer to digitiza-tion. This in turn creates opportunities for the mines tobecome completely autonomous in the future.Controlling, monitoring and planning such productionrequires new digitized solutions. Part of such a solutioncould for example be to mount different types of sensors inthe mining process. Data gathering from sensors withdiagnostics supported by predefined set-points enables earlyalarms allowing production personnel to react before a fireis a fact. This paper describes the conducted experimentalstudy aiming at identifying risk for fire caused by miningvehicles in underground mines. The test result shows thatsome types of sensors have potential to early detect firehazards.

In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in mining operations must have meticulous approaches and uncompromised security. By ensuring a secure transition, the mining industry can navigate the transformative shift towards autonomy while upholding the highest standards of safety and operational reliability. Experiments involving autonomous pathways for mining machinery that utilize AI for route optimization demonstrate a higher speed capacity than manually operated approaches; this translates to enhanced productivity, subsequently fostering increased production capacity to meet the rising demand for metals. Nonetheless, accelerated wear on crucial elements like tires, brakes, and bearings on mining machines has been observed. Autonomous mining processes will require smarter machines without humans that guide and support actions prior to a hazardous situation occurring. This paper will delve into a comprehensive perspective on the safety of autonomous mining machines by using Bayesian networks (BN) to detect possible hazard fires. The BN is tuned with a combination of empirical field data and laboratory data. Various faults have been recognized, and their correlation with the measurements has been established.