Figure 1. The bathtub curveThere are many companies that have embraced this technology for reliability improvement and have significantly reduced maintenance costs and increased the MTBR of their equipment.
What We’ve Learned About Technology and Reliability ImprovementNow let’s turn to a way to capitalize on what we have learned over the past 80 years or so by embracing the new approach of the Industrial Internet of Things (IIoT) to take condition monitoring and reliability improvement to the next level and reduce the cost of the human capital required to analyze the data. IIoT is the integration of the physical and digital world through networked sensors, machine learning and analysis of big data utilizing cloud computing. Prior to IIoT, the Internet depended on people to interface the physical world with the digital world. Microprocessors gave people the ability to analyze data much faster, but now we can bypass people and take inputs directly from sensors, send this data to the cloud and use cloud computing to analyze the data and send us the results in the form of actionable information. Industry experts predict that 50 billion machines will be connected directly to the Internet over the next five years. It’s happening all around us as we speak. Does anyone in your family wear a Fitbit? This is a great example of the kind of technology that we can apply to our industrial machines. The Fitbit sensors can measure the number of steps you take in a day, perform analysis to convert them to distance, calculate how many floors you have climbed, provide a graphic representation of your activity, determine how many calories you have burned, track how much weight you have lost and the progress you have made on your weight loss goals, and measure the amount of sleep you have gotten. It does all this seamlessly. The information is displayed on the device or through a user interface on your smartphone or computer, as shown in Figure 2.
Figure 2. Example of information provided by a Fitbit
ExampleNow let’s apply this thinking to a pump installed in your plant with sensors and controls. This pump is controlled by a variable-speed drive. It has accelerometers and temperature probes (RTDs) at each bearing location, a flow meter, and inlet and outlet pressure transmitters. All sensor and drive outputs are connected to a gateway that transmits the data to the cloud. This data is analyzed in the cloud and sent back in real-time through a user interface. It is user-name and password protected with encrypted data. This monitoring system also generates alerts based on preset triggers to specified personnel via cellphone, email or text message. Or, it creates a work order that defines specific action to be taken and sends it to the appropriate technician. The user interface in Figure 3 displays results from these sensors. This type of system can actually show you a real-time pump curve depicting where the pump is running on its curve. This all occurs without human intervention. Nobody has to travel to the pump, go to a website or take any other action.
Figure 3. User interface with sensor results from a pump for reliability improvementThis is just putting our toe in the water around this technology and reliability improvement. Cisco’s CEO has pegged the entire Internet of Things (IoT) as a $19 trillion market. The IIoT is a significant subset that includes concepts like digital oil fields, advanced manufacturing, grid automation and smart cities. General Electric and Accenture conducted a global research project. It took a pulse of the progress, challenges and opportunities of industrial companies around the world. Approximately 88 percent of those interviewed say big data analytics are in their company’s top three priorities. 53 percent say it is now a board-level decision. These companies are looking at leveraging connectivity to the Internet and data analytics to achieve business priorities. They are targeting real-world issues like increasing throughput, reducing costs, improving product quality, improving resource efficiency, shortening response times and other valuable outcomes. I have not worked with a customer who was not interested in most, if not all, of these issues. In conclusion, we would like to leave you with a few thoughts. Historically, the type of continuous monitoring system described in this paper cost tens of thousands of dollars per machine. This made it cost-prohibitive to consider using on a plant-wide basis for reliability improvement. Today, the cost can be driven below $1,000 per machine, which makes it economically feasible for a total plant solution. The other factor in cost-justification is to not only consider the cost of the repair or replacement of the machine. It is to also include the loss of manufacturing time, procurement cost and product loss. When all the costs of loss are included in the calculation, justification for reliability improvement is much easier. Consider using this system only on your critical equipment if budget is a concern. Still skeptical about the value of this approach? Try it on a few of your most critical machines. Then evaluate the results and decide where to go from there. Basically, it boils down to whether you want to use the Fitbit approach to monitor all your machines. This article was previously published in the Reliable Plant 2016 Conference Proceedings. By Tom Dabbs and Rick Zinkl, DXP Enterprises