Year: 2025
Type: article
Abstract: By enabling companies to foresee equipment faults & thereby save downtime and maintenance prices, AI-driven predictive maintenance is revolutionizing Industrial IoT (IIoT). Whether reactive or planned, traditional maintenance approaches may lead to unnecessary service or unanticipated problems that compromise the efficiency & the financially. Artificial intelligence predictive maintenance uses sensor data analysis to spot the patterns and anomalies suggestive of possible issues. This method uses their edge computing, which guarantees quick response times by means of actual time data processing close to the machines, therefore reducing the latency. Industries like manufacturing and energy that demand quick response depend on this especially. Concurrent with this is cloud computing providing the necessary infrastructure for complex AI model training, huge scale data storage & continuous prediction algorithm improvement. Combining edge and cloud technologies will help companies to strike a balance between quick on-site decisions and thorough long-term planning. This approach clearly has benefits in terms of reduced operating interruptions, better maintenance schedules, and longer equipment lifetime all of which help to save a lot of money. Still difficult are the integration of artificial intelligence with legacy systems, the necessity of better data quality, and cybersecurity concerns related with linked devices. As artificial intelligence models develop and processing capacity increases, predictive maintenance is expected to show increased autonomy, improved accuracy, and more general use across many industries
Author: Karthik Allam
Institution: JPMorgan Chase & Co (United States)
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411620407