Risk assessment of workers exposed to respirable crystalline silica in silica crushing units in Azandarian Industrial Zone, Hamadan, Iran

Introduction: Azandarian industrial zone with about 40 active silica crushing units is one of the largest industrial area in Hamadan province, Iran. Materials and methods: In this study, the personal exposure of workers in the activated silica crushing units was measured. Assessing the risk of mortality due to exposure to Respirable Crystalline Silica (RCS) in the workplace was then estimated through measuring the personnel exposure in accordance with the National Institute for Occupational Safety and Health (NIOSH) 7601 method. Moreover, the mortality rate of lung cancer and risk of mortality due to exposure to RCS were estimated. Results: Based on the results, the average exposure of employees to RCS in the crushing units was in the range of 1.70 -0.14 mg/m3. As observed, the lowest and highest exposure was obtained for the admission unit and sandstone, respectively. In general, it can be inferred that in all studied occupation positions, the exposure level was higher than the recommended standard (0.25 mg/m3). As can be seen, the carcinogenic risk level for the exposed workers was in the range 2-26/1000. The results of risk assessment showed that the highest risk level was related to the stamping machine operator unit and the lowest was related to the administrative unit. Conclusion: Therefore, the workers working in high-risk units such as stamping machine operator and stone separation operator are more likely to suffer from adverse health complications such as silicosis, lung cancer and other respiratory complications.

Improving the methods for determining the index of quality of subsystem element interaction

It was proposed to improve the existing method of determining the quality of interaction of the elements of subsystems of the Machine Operator-Machining Center-Control Program for manufacturing parts (MO-MC-CP) system. This method combines estimates of social (machine operator), technical (machining center), and informational (control program for manufacturing parts) subsystems. Improvements were achieved through the use of four independent indices which are defined separately. One index takes into account single, double and triple interactions of integrated indicators where values of specific weight of weight coefficients depend on the sample size. The other three indices are a synergistic effect where the weight coefficients do not depend on the sample size. Therefore, the model of this index was modified at the expense of additional subsystems. Existing approaches to determining the indices are not focused on the assessment of the quality of interaction of the MO-MC-CP system, have software limitations, and work with limited sample sizes. With this in mind, it was decided to improve the existing tools of determining the quality indices of interaction to assess levels of interaction of the subsystem elements. The proposed software-implemented methods and the technology of index assessment improve the efficiency of the assessment of complex systems. Experimental verification has shown the superiority of interaction quality indices over those in the existing methods. A sign of efficiency is as follows: a smaller value of mean-square deviation of the proposed indices in comparison with the existing ones: S(ІQI1)=0.812; S(ІQI2)=0.271; S(ІQI3)=0.675; S(ІQI4)=0.57 and S(І)=0.947; S(І)=0.833; S(І)=0.594, respectively. The results obtained in the study of the interaction quality index are useful and important because they make it possible to better assess the interaction of subsystem elements and apply the proposed technology at industrial enterprises.

Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

In this paper, the problem of the identification of undesirable events is discussed. Such events can be poorly represented in the historical data, and it is predominantly impossible to learn from past examples. The discussed issue is considered in the work in the context of two use cases in which vibration and temperature measurements collected by wireless sensors are analysed. These use cases include crushers at a coal-fired power plant and gantries in a steelworks converter. The awareness, resulting from the cooperation with industry, of the need for a system that works in cold start conditions and does not flood the machine operator with alarms was the motivation for proposing a new predictive maintenance method. The proposed solution is based on the methods of outlier identification. These methods are applied to the collected data that was transformed into a multidimensional feature vector. The novelty of the proposed solution stems from the creation of a methodology for the reduction of false positive alarms, which was applied to a system identifying undesirable events. This methodology is based on the adaptation of the system to the analysed data, the interaction with the dispatcher, and the use of the XAI (eXplainable Artificial Intelligence) method. The experiments performed on several data sets showed that the proposed method reduced false alarms by 90.25% on average in relation to the performance of the stand-alone outlier detection method. The obtained results allowed for the implementation of the developed method to a system operating in a real industrial facility. The conducted research may be valuable for systems with a cold start problem where frequent alarms can lead to discouragement and disregard for the system by the user.

MODELING OF SOME DYNAMIC CHARACTERISTICS OF TECHNOLOGICAL MACHINES OF THE ROAD-BUILDING COMPLEX

Рассмотрены варианты моделирования управления мобильными технологическими машинами строительного комплекса для различных задач автоматизации рабочих процессов с целью уменьшения выбросов в атмосферу за счет сокращения расхода топлива. Представлены возможные схемы снижения вредных вибрационных воздействий на оператора машины. На основе анализа динамических характеристик в операторной форме и с учетом передаточных функций, рассмотрены требования к подвеске технологической машины, повышающие защиту машиниста от воздействия вибраций при автоматическом управлении рабочими процессами. Приведены функциональные схемы динамических систем, позволяющие учитывать изменение упругих и демпфирующих свойств обрабатываемой поверхности грунта. We considered some variants of modeling control of mobile technological machines of the construction complex for various tasks in automation of work processes in order to reduce emissions into the atmosphere by reducing fuel consumption. We presented as well possible schemes for reducing harmful vibration effects on the machine operator. Being based on the analysis of dynamic characteristics in the operator form and taking into account the transfer functions, we considered the requirements for the suspension of a technological machine. This increases the protection of the driver from the effects of vibrations during automatic control of work processes. Functional schemes of dynamic systems are given, which allows us to take into account changes in the elastic and damping properties of the processed soil surface.

Methodological approach for the development of an operator assistance system for the press shop

The productivity of a deep drawing process strongly relies on its robustness as well as the experience of the machine operator. Steadily increasing requirements regarding weight, design and efficiency lead to a production operating increasingly closer to the process limits, making it more challenging to ensure a high robustness of the process. Minimal process fluctuations caused by disturbances such as varying material properties or changing tribological conditions may negatively affect the process due to deteriorated product properties as well as an increased risk of scrap. Thus, a target-oriented adjustment of available parameters by the machine operator becomes more difficult, and an increased knowledge about the causes of defects is more important. In the past, several approaches with different combinations of sensors and actuators have been investigated to enable a stable process window based on a control system. This paper presents a method to address the need for a more robust process by developing an operator assistance system that enables the identification of the component state and provides decision support to the machine operator. The methodological approach includes a thorough process analysis to evaluate the expediency of such a system and to make a reasonable preselection of sensors in order to avoid unnecessary costs.

A novel tool monitoring approach for diamond wire sawing

Mobile diamond wire sawing is a highly flexible, productive and, versatile cutting process. Accordingly, it is used in many areas, such as the dismantling of nuclear power plants or wind turbines. Despite the widespread use of the process, the cutting process requires continuous manual monitoring by the machine operator. This is due to the continuously changing cutting conditions. A common process error is tool breakage. It is often caused by the displacement of the grinding segments (cutting beads). Due to the cutting speed (up to 30 m/s), these failures cannot be detected and prevented by the machine operator. However, a measuring system or process monitoring does not exist yet. Accordingly, a damaged diamond wire can become hooked, which often results in wire breaks. As a result, grinding segments break away from the wire, which can lead to deadly accidents. Therefore, a new approach for monitoring the tool for diamond wire grinding will be investigated. The paper is divided into five sections. First, the requirements for the sensor system are derived. After the selection of a measuring principle and the functional verification in the grinding process, the monitoring approach is presented and features for monitoring the tool with regard to the displacement of grinding segments are described. It was shown that the developed approach is suitable for monitoring the diamond wire tool during the sawing process. The investigation on a prepared diamond wire tool also demonstrated that the feature allows the detection of displacing grinding segments already from 2 mm.

Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

The current digitization of industrial processes is leading to the development of smart machines and smart applications in the field of engineering technologies. The basis is an advanced sensor system that monitors selected characteristic values of the machine. The obtained data need to be further analysed, correctly interpreted, and visualized by the machine operator. Thus the machine operator can gain a sixth sense for keeping the machine and the production process in a suitable condition. This has a positive effect on reducing the stress load on the operator in the production of expensive components and in monitoring the safe condition of the machine. The key element here is the use of a suitable classification model for data evaluation of the monitored machine parameters. The article deals with the comparison of the success rate of classification models from the MATLAB Classification Learner App. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the CNC vertical machining center (CNC-Computer Numerical Control). Three basic states representing machine tool damage are recognized. The data are then processed and reduced for the use of the MATLAB Classification Learner app, which creates a model for recognizing faults. The article aims to compare the success rate of classification models when the data source is a dataset in time or frequency domain and combination.

Regression Approach to a Novel Lateral Flatness Leveling System for Smart Manufacturing

Sheet metal coils are widely used in the steel, automotive, and electronics industries. Many of these coils are processed through metal stamping or laser cutting to form different types of shapes. Sheet metal coil leveling is an essential procedure before any metal forming process. In practice, this leveling procedure is now executed by operators and primarily relies on their experience, resulting in many trials and errors before settling on the correct machine parameters. In smart manufacturing, it is required to digitize the machine’s parameters to achieve such a leveling process. Although smart manufacturing has been adopted in the manufacturing industry in recent years, it has not been implemented in steel leveling. In this paper, a novel leveling method for flatness leveling is proposed and validated with data collected by flatness sensors for measuring each roll adjustment position, which is later processed through the multi-regression method. The regression results and experienced machine operator results are compared. From this research, not only can the experience of the machine operators be digitized, but the results also indicate the feasibility of the proposed method to offer more efficient and accurate machine settings for metal leveling operations.

Navigation system for heading machines in potash mines

Mining operations at potash mines are carried out by heading machines. Setting of a direction and control of the movement of the machines is carried out by the mine surveyor and by the machine operator in the manual mode. The lack of automation of this process during production leads to large labor costs of the mine surveying service, while the experience of the machine operator affects accuracy of maintenance of a specified course. Currently, there are no ready-made technical products for automating the process of setting the course and controlling the movement of heading machines. This paper deals with the implementation of the navigation system for heading machines in the underground mines of Uralkali company. At the mines of Uralkali, the requirements for the accuracy of such a system are dictated by the requirements for the accuracy of mine surveying support for underground mining operations in driving new roadways. Possible ways of constructing navigation systems and the problems of their application are considered. The analysis of the existing methods shows that the most promising option for navigation of heading machines in underground mine openings are the systems based on the principles of inertial navigation. To use such systems in underground mines and to ensure the required accuracy, the technical requirements for the systems are formulated. It is shown that modern strapdown inertial navigation systems satisfy the required accuracy. On their basis, a prototype of the heading machine navigation system was developed, and its ground tests were carried out. The achieved accuracy of the system makes it possible to proceed to testing of a real heading machine in a mine. The study was supported by the Russian Science Foundation, Project No. 19-77-30008.