# A Model for Working Environment Monitoring in Smart Manufacturing
## 2 Related Work
[14] presented an interactive visual analytics system for data driven equipment condition monitoring in smart production systems. The system helps managers and operators at manufacturing sites to use collected domain knowledge for their decisions and work. Experts from industry participated in the design of the system, and helped in closing the gap between the industrial needs and available technologies. The experts' experience helped in determining the large volume of manufacturing data to be monitored and the appropriate sensors to be deployed for monitoring critical tasks. The sensors were grouped in modules, which enabled efficient monitoring and generation of vectors with the collected data. A condition reference library (a training set) was constructed based on previous experience and it enabled training of the monitoring model. The user can refine the monitoring model by inspecting and updating the training set in order to adapt to various changing conditions in the manufacturing system. This enables users to select the most appropriate sensors and use them in constructing the monitoring model. The monitoring model is based on a cluster with 19 data nodes and 342 cores that support collecting and processing a large amount of manufacturing data. The monitoring system was evaluated on a dataset with over 0.42 billion records collected from 791 sensors in a petrochemical plant. A study presenting the measurement of harmful and hazardous factor values of noise, air dust in the production environment, vibration, and humidity in the workplace in glass product manufacturing was shown in [18]. A set of data collected from sensors was used as an input for an algorithm for substantiating managerial decisions to minimize the negative impact of workplace physical factors. The authors presented the structure of the system for monitoring working conditions with sensors, analogy-frequency converters, and a data processing center. The data processing center hosted software with the following functions: administration and configuration of the whole monitoring system, storing collected data, data processing (visualization, reporting, verification, analysis, and modeling), interaction with other systems, and information security and data integrity. An algorithm was implemented for monitoring workplace physical factors in the workshops for the transportation, preparation, and mixing of materials in glass product manufacturing. Based on the evaluation results, the authors concluded that the proposed algorithm and system enabled the identification of deficiencies in the working environment and the development of action plans for their improvement. [PERSON] et al. [21] proposed an edge-fog-cloud architecture for detecting thermal anomalies in aluminum factories. Sensing was performed with mobile IoT edge nodes carried on autonomous robots, while using drones as fog nodes increased the responsiveness of the system and reduced surveying costs. Analysis of thermal anomalies was performed in a cloud back-end. Limitations of the cloud computing part of the system were exceeded by use of mobile drones, which enabled a distributed computing architecture and improvement of the network utilization. Measurement of the temperature was performed at different locations in order to help in studying and analyzing the thermal balance of the posthell in the factory. The detections and localizations of thermal anomalies helped operators to activate a cooling system, and to increase the safety of workers and the factory. The system was tested in the laboratory and in the factory. Test results revealed that the proposed architecture based on mobile sensing with robots reduced operation costs and system response type, while increasing the accuracy of the detection of thermal anomalies compared to human surveyors or fixed IoT node monitoring.