Equipment manufacturers commonly face the challenge of keeping machinery, and other assets in effective working condition while also reducing the costs of maintenance and time-based repairs.
Considering the aggressive time-to-market for products and services, it’s becoming increasingly important to identify the cause of their possible faults or failures before they occur. Emerging technologies like the Internet of Things, big data analytics, and cloud are enabling industrial equipment, and assembly robots to convey their current status to a centralized server making detection of faults easier, more practical, and more direct.
By proactively identifying potential issues, companies can 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, and in other unstructured data. The latter comprises but is not limited to information from millions of log entries, sensors, error notifications, pressure, current, voltage, odometer readings, and engine power-torque specifications.
By using advanced data analytics, the information derived from such sources can be turned into meaningful and actionable insights for pro-active maintenance of assets to prevent incidents that result in asset downtime or accidents, monitor asset behavior, and tune assets for their peak performance levels. Predictive maintenance is a scientific procedure to forecast when a functional equipment will fail, or even deviate from its normal behavior so that its maintenance and repair can be scheduled before the failure occurs.
The underlying architecture of a preventive maintenance model is fairly uniform irrespective of its end applications. The analytics usually resides on a host of IT platforms, but systematically these layers can be described as:
- Data acquisition-by embedding suitable sensors and operational log files.
- Data transformation—conversion of raw data for machine learning models
- Condition monitoring—offering alerts as per operating limits of assets
- Asset health evaluation—generating diagnostic records based on trend analysis if health of an asset has already started declining
- Prognostics—generating predictions of failure through machine learning models and estimating remaining life
- Decision support system—recommendations of best actions
- Human interface layer—making all information accessible in easy-to-understand forms
Failure-type classification, fault diagnosis, failure prediction, and recommendation of relevant maintenance actions are all parts of predictive maintenance methodology.
As industrial customers become increasingly aware of the growing maintenance costs and downtime caused by the unexpected breakdown of machinery, predictive maintenance solutions are gaining traction. The bigger players have already been using this methodology for more than a decade. Today, the technology is mature and modularized so the small and medium-sized companies in the manufacturing sector can also reap its advantages by keeping their repair costs low and meeting initial operational costs for new operations.
While it evidently offers more business benefits than corrective maintenance, predictive maintenance is also a step ahead of preventive maintenance—the maintenance work is scheduled at preset intervals and intended to reduce the probability of failure or the degradation of an asset’s functions.
In addition to the advantages of controlling repair costs, avoiding warranty costs for failure recovery, reducing unplanned downtime and eliminating the causes of failure, predictive maintenance employs non-intrusive testing techniques to evaluate and compute asset performance trends. The methods used can be thermodynamics, acoustics, vibration analysis, and infrared analysis among others.
The continuous development in big data, machine-to-machine communication, and cloud technology has created new possibilities of studying the information emanated by industrial assets. Condition monitoring in real time is viable thanks to inputs from sensors, actuators, and other control parameters. What the stakeholders need is a bankable analytics and engineering service partner who can help them to leverage data science to not just predict embryonic asset failures but to eliminate them and take action in a timely way.
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