Articles

IOT-Powered Substation Surveillance System

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 An IoT-based substation monitoring system provides an intelligent and efficient means for the continuous, real-time observation of critical parameters in power substations, overcoming the shortcomings of conventional monitoring that predominantly depends on manual inspection. This system incorporates IoT sensors to continually monitor voltage, current, temperature, humidity, and gas levels (including SF6), hence evaluating the operational integrity of substations. The gathered data is conveyed across secure networks to a central server or cloud platform for processing and analysis. The system use predictive analytics to detect early indicators of equipment failure or performance irregularities, activating warnings for prompt intervention. This reduces downtime, averts expensive repairs, and prolongs the lifespan of essential substation components by facilitating condition-based maintenance instead of conventional, time-based inspections. Operators can obtain real-time data and alarms via an intuitive online or mobile interface, enabling remote monitoring of substations and facilitating timely, data-informed choices. The IoT-based monitoring system improves operational safety, efficiency, and reliability in energy distribution through remote oversight and rapid response capabilities. This technology-centric strategy not only diminishes maintenance expenses and enhances asset management but also fortifies the power grid’s resilience, allowing it to satisfy escalating demands for stability, automation, and efficiency.

Marketing Strategy for Online Condition Monitoring (Case Study: Nanoprecise Machine Doctor)

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Today’s industry is facing ever-increasing competition. To stay competitive, a company needs to maintain the high reliability and productivity of its assets. Companies must invest in maintenance programs to prevent unplanned downtime and reach their optimal reliability. Predictive maintenance or condition-based maintenance is an important aspect of a maintenance program to maintain asset reliability. One emerging predictive maintenance tool, fueled by digital disruption, is online condition monitoring. Online condition monitoring provides diagnostics with shorter intervals than walking survey analysis with portable analyzers, allowing it to diagnose faults not detectable by other condition monitoring methods.

Adikari Wisesa Indonesia, a firm specializing in maintenance services, has partnered with Nanoprecise Sci Corp and is the sole distributor of Machine Doctor sensors in Indonesia to enhance its maintenance service. However, the sales of Machine Doctor were suboptimal. This study aims to identify the business issue, provide analysis, and propose a solution to the business issue.

The market is analyzed using the STP framework. Then, the general environment (PESTEL), industry environment (Five Forces), and competitor analysis are performed to better understand the external environment Adikari Wisesa is currently in. After analyzing the external environment, the internal environment of Adikari Wisesa is studied by using a Resource-Based View, VRIO, and Value Chain Analysis. Then, a SWOT analysis is performed summarizing the business situation of the company. A business solution is then proposed based on a TOWS matrix.

Predictive Maintenance Solution for Logistics Robotic Cell

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The Industry 4.0 trend is known as being the next industrial revolution. The major change introduced by this concept, is the digitalization of the industry. The manufacturing field that has been already transformed into automation (being considered as the third industrial revolution) need to be connected, to be gathered data based on witch new business value is expected. The logistic domain suffered also a lot of change along the last years. Robotic cells are deserving logistic chains and maximizes the outcome. Together with the use of robotic cells, machines are used as a service, in which they are being paid as much as they produce. Robots cells as a service, in the word of selling everything as a service. For this business model, predictive maintenance is an important aspect, since a not working cell, can’t generate value and revenue. Current research approaches the predictive maintenance solution for logistic robotic cell, in order to increase the uptime of the machine, and therefore the output. Logistic machine producers sell the cells as a service and assumes all the risks that appear.