COMBINATORY APPROACH TO FAILURE FINDING IN PASSENGER CAR ELECTRICAL EQUIPMENT SYSTEM

Maintaining a high level of maintenance of electrical equipment in passenger cars requires research in the field of improving methods and means of technical diagnostics. Electronic devices for automatic control and protection, which are located in the switchboard of the car, are one of the most critical elements in the electrical equipment system, ensuring its reliable, efficient and safe operation. If the automatic adjustment and protection devices are out of regulation during operation, the voltage and current in the electrical equipment network may exceed the permissible level, which leads to the failure of electricity consumers, a battery and a generator. In addition, the semiconductor devices themselves are very sensitive to short-term overloads, in which, even with minor overloads, a breakdown or breakage of the conductive layer occurs. Modern trends in the development of passenger car fleet indicate a sharp increase in the complexity of the element base of cars. This mainly concerns the electrical systems of cars, where all the functions of control, monitoring and diagnostics are performed by electronics. Maintaining a high level of service for such systems requires research in the field of improving methods and means of technical diagnostics. For these purposes, the authors have developed a method for constructing optimal control and diagnostic tests, which is based on a combinatorial approach. The method is easily implemented on a computer.

CONTROLLED TESTING OF BELT TRANSMISSIONS AT DIFFERENT LOADS

The presented paper provides the alternative options for determining the condition of belt gear based on the testing and monitoring. In order to carry out experimental measurements, a newly developed device for testing, monitoring and diagnostics of belt drives was designed, as well as the possibility of determination of limit states by extreme loads. The designed measuring stand allows to determine the point of destruction of the belt for an extreme load. The process of the belt measurement was to set the predetermined input revolutions of the driving motor by means of the Altivar 71 (FM1) frequency inverter connected to the driving electric motor. The next step in defining the input parameters was to set the load on the driven electric motor. The load on the driven motor was achieved by the torsional moment set by means of the Altivar 71 (FM2) frequency inverter connected to the driven electric motor. In the paper, the analytical calculation is processed. The article mainly points to the innovation of the stand for testing belt transmissions.

Connecting Reservoir, Wells, Facilities Management, HSEE to Accelerate Data Driven Value: Digital Fields Expansion

Oil & gas industry player have always been big investors in advancement of technology, especially in the direction of extracting additional petroleum to address the production decline. In the spirit of automation, PETRONAS has various automated technical workflows that tackles different types of challenges and purposes. The operational, technical and engineering aspects of increasing production and effectiveness of execution are built upon these processes related to automation of data sources as well as systems integration. With the recent challenge that forced the employees to work remotely, it is now more important than ever to ensure that the Digital Fields (DF) solution can cater for more information and to transform the way of working. Linking distant teams to work together on the same platform to resolve production related issues, centralized monitoring and diagnostics is key to this transformation. Workflows can enable organizational vision since having the right type of information available in a visualization environment that provides actionable insights to the right "persona" across different domains and teams accelerates production increases and decreases the production decline at brown fields. The success of this is linked with working together with the Reservoir, Wells and Facilities Management (RWFM) team to ensure the critical information are captured. The improved synergy between offshore and onshore staff due to the shared operations visualization supports further analysis and decision making irrespective of their location. Providing the "persona" with the relevant production and other related data in a modern analytical platform allows them to concentrate on production optimization rather than the data gathering aspect of the traditional method. PETRONAS has considerable experience in developing automated digital oilfield workflow solutions and extending Digital Fields capabilities with greater coverage of other systems such as Health, Safety, and Environment (HSE) and topside facility management is part of the current and future roadmap. In this paper, we will describe the journey taken by PETRONAS Upstream Digital in extending the Digital Fields capability, and how the effort in digital transformation has helped in unlocking greater value in the daily operation.

Network Management System for IoT Based on Dynamic Systems

The Internet of Things (IoT) has the potential to transform the public sector by combining the leading technical and business trends of mobility, automation, and data analysis to dramatically alter the way public bodies collect data and information. Embedded sensors, actuators, and other devices that capture and transmit information about network activity in real-time are used in the Internet of Things to connect networks of physical objects. The design of a network management system for an IoT network is presented in this paper, which uses the edge computing model. This design is based on the Internet management model, which uses the SNMP protocol to communicate between managed devices, and a gateway, which uses the SOAP protocol to communicate with a management application. This work allowed for the identification and analysis of the primary network management system initiatives for IoT networks, in which there are four fundamental device management requirements for any deployment of IoT devices: provisioning and authentication, configuration and control, monitoring and diagnostics, and software updates and maintenance.

Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler

Belt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods.

Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

This paper describes the method developed using the Extreme Gradient Boosting (Xgboost) algorithm that allows high-resolution imaging using the ultrasound tomography (UST) signal. More precisely, we can locate, isolate, and use the reflective peaks from the UST signal to achieve high-resolution images with low noise, which are far more useful for the location of points where the reflection occurred inside the experimental tank. Each reconstruction is divided into two parts, estimation of starting points of wave packets of raw signal (SAT—starting arrival time) and image reconstruction via XGBoost algorithm based on SAT matrix. This technology is the basis of a project to design non-invasive monitoring and diagnostics of technological processes. In this paper, we present a method of the complete solution for monitoring industrial processes. The measurements used in the study were obtained with the author’s solution of ultrasound tomography.

Adaptive Control of the Metalworking Process With Using Sound Generated During Cutting

The presented paper proposes a method of adaptive control of the metal cutting process based on the analysis and interpretation of recorded sound for the identification of the cutting process. The presented research results clearly demonstrated the efficiency of using the sound generated during cutting by CNC lathe 16 K20T1 for the adaptive metalworking control. The sound makes it possible to control the accuracy of the workpiece part geometry and the surface quality in the online mode. The online mode also makes it possible to make an informed decision about a timely interruption of the cutting process or the continuation of the process in the altered modes for its successful completion. As the research results presented in the paper show, there is a close correlation between the sound generated by cutting and the roughness of the surface being machined or the degree of wear of the cutting tool. The correlation between the sound and roughness of the machined surface is 0.94, whereas between the sound and wear of the cutting tool is 0.93. The paper aims to use the generated sound as operational information needed for adaptive control of the metal processing process and timely monitoring and diagnostics of the condition of processed materials using the precision index, newly introduced as the machining quality index.

Automated complex of intelligent monitoring of a solar power plant

A solution for automated monitoring and diagnostics of photovoltaic modules of industrial solar power plants is proposed. The solution is based on the use of an unmanned aerial vehicle with a specialized payload and a ground-based intelligent information and control system to detect problem areas of the station, in particular partial shading and pollution. To perform the detection procedures, a neural network based on the Fast R-CNN architecture with the learning algorithm – Inception v2 (COCO) was used. The results of preliminary tests showed that the accuracy of detecting problem areas is at least 92%. The article presents a mathematical model that allows calculating the installed power monitored by the complex, depending on the type of station and UAV, meteorological parameters, and the performance of computing equipment. Numerical calculations have shown that when using the FIMI X8SE UAV and a computing device based on the RTX2080 GPU, the installed monitored power will be up to 7.5 MW.