Business Description

Market Research Future reports that the global predictive maintenance market is expected to grow due to increasing focus on reducing operational costs and asset downtime. Our software using the data gathered through the use of various condition monitoring sensors and techniques and Artificial intelligence(A.I.), has paved the way to do auto predictive maintenance warnings. Predictive maintenance relies on condition-monitoring equipment to assess the performance of assets in real-time. Implementing Internet of Things (IoT), PdM creates an accurate tool for collecting and analyzing asset data by combining condition-based diagnostics with predictive formulas. Physical production systems generate data that can be used to benefit the production line. The massive amount of available data can be utilized to create data-driven solutions for model-based anomaly detection, anomaly localization and predictive maintenance. Models that represent the normal behavior of the system are trained from source data from operating manuals, data collected from sensors and control signals to create the "perfect data." Then, live data from the system can be compared to the "perfect data" and predictions of the model to detect faults, perform fault diagnosis and drive the overall condition of a system or its components. By using IoT technology, different sensors can collect and share data while most commonly, measure vibration, noise, temperature, wear and tear but can go beyond and measure things like electrical currents and corrosion. These sensors connect the assets to a central system that stores the information coming in and is run using cloud technology. This exchange of information is at the core of predictive maintenance.