Keeping fleet, machinery and other assets working efficiently is a common challenge among equipment manufacturers, engineering, procurement and construction (EPC) companies, and power and process plant owners and operators. All the more complicated is simultaneously reducing costs of maintenance and time-sensitive repairs. Aggressive time-to-market for industrial products and services makes it even more critical to identify the cause of potential faults or failures before they have an opportunity to occur. Emerging technologies like the Internet of Things, big data analytics, and cloud data storage are enabling more vehicles, industrial equipment and assembly robots to send condition-based data to a centralized server, making fault detection easier, more practical and more direct.
Identifying potential issues in a proactive manner allows companies to deploy their maintenance services more effectively and improve equipment up-time. The critical features that help to predict faults or failures are often buried in structured data, such as year of production, make, model, warranty details, as well as unstructured data such as maintenance history and repair logs.