Vishwanadham Mandala’s scientific contributions

Predictive maintenance (PdM) predicts machine failures in heavy-duty vehicles with diesel engines. PdM utilizes deep learning algorithms on vast amounts of Internet of Things (IoT) data to forecast potential failures accurately. However, the sheer magnitude and rapidity of data generated makes this process incredibly expensive. We propose a novel model executed on Amazon Web Services (AWS) IoT and Kafka Streams to mitigate this challenge. Through our extensive experiments, we confidently demonstrate the effectiveness and efficiency of our approach, including the successful implementation of the activation threshold parameter, resulting in significantly enhanced prediction accuracy. Moreover, we introduce a valuable assessment (VA) method for evaluating the incidence rate scale, further enhancing our predictive capabilities. The results obtained from our comprehensive analysis highlight the superior performance achieved through a meticulously balanced VATP and VA strategy, establishing our solution as a game-changer in predictive maintenance for heavy-duty vehicles.

Our comprehensive Machine Learning (ML) and Artificial Intelligence (AI)-based patent analysis in braking, parking, and related technology areas will reveal the most significant opportunity clusters relevant to these domains. We can use predictive analytics to forecast the development of currently identified opportunity clusters, employing an innovation competition model. This paper is organized as follows: In the first two sections, we systematically reveal how mature ABS, ESC, and parking technologies have settled in the global automotive market, using extensive patent data between 1966 and September 2019. In the third section, after disclosing the structure of our Telecommunications Engineering Centre (TEC), we detail our AI and ML analysis, focusing on the domains determined by our patent categorization strategy. Finally, the last section gives a high-level concluding discussion, summarizing the present approach and its significant contributions. Safety has always been a priority in designing and developing automotive systems. For instance, the function of an anti-lock brake system (ABS) is to prevent the wheels from locking up, thus enabling the driver to maintain steering control. Since its invention, ABS has become a standard safety feature in all vehicles. Since 2019, 65% of passenger car production globally has been equipped with some ABS. Most of ABS development has been accomplished by adding mechanical, electrical, and hydraulic components to vehicles. Although hardware development, like sensor technology, is essential, many breakthroughs have been achieved by inventing a control technique for ABS: FI (complete integral) control in 1978. This success has introduced disproportionate amounts of Intellectual Property (IP) around control algorithm innovation to bring more ABS products to market. Over time, competition occurs in other domains, such as the control algorithm, sensor technology, and system integration, and customers are looking for algorithm innovation to bring them better performance, lower cost, and compact solutions.