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How Predictive Maintenance Is Improving Manufacturing

With predictive maintenance, manufacturers have real-time visibility into the current state of production equipment and can receive automated alerts when machines are ready for service or aren’t functioning properly. Using data gathered from machine-mounted sensors, AI and machine learning algorithms prescribe customized maintenance schedules based on how production equipment is actually being used. As an integral part of Industry 4.0, predictive maintenance technology uses data to fuel gains in efficiency and profitability. 

What Is Predictive Maintenance?

Predictive maintenance is a proactive, data-driven approach for maintaining industrial equipment. Using a network of connected sensors to monitor in-service equipment, operational data is collected, transmitted, and analyzed. Predictive maintenance programs use the results of this analysis to automatically generate service alerts when equipment is operating in suboptimal condition or will soon require routine maintenance based on regular patterns of use.

Predictive maintenance vs. preventive maintenance 

Preventive maintenance involves serving equipment at regular, predefined intervals. This traditional approach to maintenance has some significant drawbacks. Without continuous monitoring, there’s no mechanism for detecting and preventing unexpected failures in between scheduled services. This can create costly unplanned downtime as replacement parts are sourced and installed. 

Predictive maintenance assumes that a one-size-fits-all approach doesn’t fully account for all of the operating conditions that can influence the type and frequency of required maintenance. Predictive maintenance uses data to monitor equipment health and prescribe a custom plan for maximizing uptime and extending equipment life.

How predictive maintenance works

The foundation of predictive maintenance is an interconnected network of machine-mounted sensors. Depending on the type of equipment being monitored, these sensors may measure factors such as noise, temperature, vibration, internal pressure, or oil level. A centralized information storage system collects the transmitted data for analysis and enables the sensors to communicate with each other and work together. 

Predictive algorithms use predetermined rules that compare actual performance against expected performance, allowing them to identify anomalies that may indicate an impending equipment failure, automatically sending maintenance alerts to factory operators. These algorithms use real-time operating conditions and historical trend data to pinpoint the optimal time a piece of equipment will require repairs, service, or replacement. 

Benefits of Predictive Maintenance 

Investment in preventive maintenance systems yields substantial ROI. According to Deloitte, manufacturers using this technology have reduced their equipment breakdowns by 70% and have 25% lower maintenance costs than those who have yet to adopt this approach. Here are five significant benefits that predictive maintenance offers:

1. Improved equipment reliability and lifespan

When implemented consistently over time, predictive maintenance programs can substantially reduce how often machinery malfunctions. Machine learning algorithms improve their forecasting accuracy over time as they compare current data with historical trends. The result is an increasingly accurate maintenance schedule tailored to how production machinery is being utilized. Machines that receive optimal care operate more efficiently and last longer than ones that receive only as-needed maintenance.

2. Reduced unplanned downtime

Unplanned downtime results in financial loss and jeopardizes production timelines. With the ability to monitor the health of production machinery from within, sensors can detect issues near instantly, allowing for less-invasive repairs that create far less downtime than a major system failure. When an issue is caught early, required adjustments can be often made while the machine is running, eliminating downtime altogether. 

3. More-efficient production lines

Machines that aren’t operating efficiently can slow production rates and jeopardize the quality of the components they produce. For example, vibration sensors can be tuned to detect the ideal vibration rhythm a piece of equipment produces when it is operating at peak performance. These sensors alert maintenance personnel when a variance is detected that indicates the machine is no longer operating at optimal levels and may require repairs.

4. Safer operating conditions 

Malfunctioning production equipment can endanger human operators. The proactive monitoring approach built into the predictive maintenance model is designed to catch and address potential safety issues before they endanger production staff.

5. Reduced maintenance costs

Predictive maintenance programs can detect problems before small issues balloon into system failures. In addition, precise maintenance predictions eliminate the need to stock unnecessary replacement parts, reducing carry costs and freeing excess business capital for deployment elsewhere.

Real-World Applications of Predictive Maintenance

Predictive maintenance is a broad and excitingly diverse field. Here are three ways it’s being applied to move manufacturing operations forward:

Monitoring internal cooling systems and heat exchangers

Cooling systems and heat exchangers can go offline quickly if a blockage prevents the liquids inside from moving through them efficiently. Internal temperature and flow sensors provide real-time readings, allowing predictive algorithms to compare current conditions to a set of preestablished parameters. When these systems begin operating outside of expected norms, automatic alerts are generated, allowing operators to intervene to prevent a breakdown. 

Maintaining manufacturing robots

Many manufacturing operations rely on industrial robots to complete tasks that are too complex or dangerous for humans. These robots often play key roles in the assembly process, making it imperative they remain operational. Monitoring metrics such as the temperature of critical components, errors with positioning, and even output quality can alert technicians to the signs of impending failure. 

Quality assurance 

Predictive maintenance and quality assurance are integrally connected. Data captured by machine-mounted sensors can also have valuable quality assurance applications. Measurements including the temperature of the production environment, consistency of torque values to ensure bolts and screws are tightened to spec, and machines that are operating outside of expected ranges can alert QA teams of potential quality issues before potentially defective products are shipped to customers.

Powering Predictive Maintenance with the Snowflake Data Cloud

Predictive maintenance harnesses the power of data to improve margins for manufacturers by reducing unplanned downtime and uncovering new opportunities to boost efficiency. The Snowflake Data Cloud accelerates Industry 4.0 initiatives such as predictive maintenance with its elastic scale and native support for semi-structured, structured, and unstructured high-volume Internet of Things (IoT) data. Harness the power of machine learning and real-time data to predict and resolve equipment performance issues faster and more efficiently than ever. Grow your manufacturing operation with the power of the Data Cloud.