Predictive Maintenance Improves Efficiency

Equipment manufacturers; engineering, procurement and construction (EPC) companies; as well as power and process plant owners and operators have the common challenge of keeping their fleet, machinery and other assets working efficiently, while also reducing the cost of maintenance and time-sensitive repairs.

 

Considering the aggressive time to market required for industrial products and services, it is crucial to identify the cause of potential failures before they 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.

By proactively identifying potential issues, companies can deploy their maintenance services more effectively and improve equipment uptime. 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 other unstructured data. The other data comprises information from millions of log entries, sensors, error notifications, pressure, currents, voltage, odometer readings and engine power torque specifications.

By generating advanced data analytics, these numbers can be turned into meaningful and actionable insights for proactive maintenance of assets, thereby preventing incidents that result in asset downtime or accidents. Called predictive maintenance, this scientific procedure enables organizations to forecast when or if functional equipment will fail so that its maintenance and repair can be scheduled before the failure occurs.

Due to higher IT spending by companies looking to optimize operating costs and increase profitability, North America will continue to be the biggest market for predictive maintenance software. With an estimated market share of 31.67%, North America is expected to grow at a compound annual growth rate of 24.5%, maintaining its lead from 2017 through 2022. Key players include Bosch, GE, Hitachi, Honeywell and Rockwell Automation.

Increasing Product Availability

The underlying architecture of a preventive maintenance model is fairly uniform. The analytics usually reside on a host of IT platforms, but these layers are systematically described as:

• data acquisition;
• data transformation—conversion of raw data for machine learning models;
• condition monitoring—alerts based on asset operating limits;
• asset health evaluation—generating diagnostic records based on trend analysis if asset health has already started declining;
• prognostics—generating predictions of failure through machine learning models, and estimating remaining life;
• decision support—recommendations of best actions; and
• human interface layer—making all information accessible in an easy-to-understand format.

Failure prediction, fault diagnosis, failure-type classification and recommendation of relevant maintenance actions are all part of a predictive maintenance methodology.

Predictive maintenance solutions will gain more traction as industrial customers become aware of the growing maintenance costs and downtime caused by unexpected machinery failures.

The bigger players have been using predictive maintenance for more than a decade. Small- and medium-sized companies in the manufacturing sector also can reap its advantages by keeping repair costs low and meeting initial operational costs for new operations.

Although it evidently offers more business benefits than corrective maintenance, predictive maintenance is also a step ahead of preventive maintenance, as maintenance work is scheduled at preset intervals to reduce the probability of failure or the degradation of an asset’s functions.

Gain the Benefits of Predictive Maintenance

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 nonintrusive testing techniques to evaluate and compute asset performance trends.

The continuous developments in big data, machine-to-machine communication and cloud technology have created new possibilities for deriving information from industrial assets. Condition monitoring in real time is viable thanks to inputs from sensors, actuators and other control parameters. What stakeholders need is a bankable analytics and engineering service partner who can help them leverage data science not only to predict embryonic asset failures, but also to eliminate them and take action in a timely manner.