It is estimated that by 2020 there will be as many as 50 billion devices connected to the Internet of Things (IoT) operating in the consumer and industrial world. Additionally, there may be as much as 44 trillion gigabytes of information transferred, utilized and analyzed. In today’s major technological advances in plant assets, there is a significant increase in data that can be captured and transferred using the IoT. Modern sensors can be used for vibration monitoring; non-invasive motor testing; ultrasonic, electrical capacitance tomography; infrared thermography; oil analysis; and much more. Many of these sensors can be attached to equipment by magnets, screws or adhesives to provide feedback on the health of those assets. The goal of using these sensors is to monitor asset condition and take corrective action when anomalies are detected. Preventing equipment failure can provide tremendous bottom-line savings. With this information available, it is important to ensure that corrective action serves a proactive purpose rather than reactive. As organizations further develop Industrial Internet of Things (IIoT) strategies for their operations, they will be increasingly analyzing tremendous amounts of data. This has forced some organizations to create the role of chief data officer (CDO) to help decipher this immense amount of information, as well as develop proactive plans and key performance indicators (KPIs) to improve their operations. For example, a plant with annual revenues of $700 million may spend as much as 5 percent of revenues on maintenance, repair and operations (MRO) to keep vital assets running. Reducing these costs by 2 percent by incorporating advanced asset monitoring and proactive strategies can reduce overall expenditures by $14 million a year. Some suppliers provide systems that improve data management to help put necessary information into the hands of maintenance personnel immediately.
IoT information allows end users to provide corrective action that serves a proactive purpose, rather than reactive.
Flowrox Inc.
03/17/2017
Figure 1. IIoT-equipped flotation loop depicting a smart valve failure. All service history of the valve, manuals, safety documents, drawings and spare parts can be viewed from any smartphone, tablet or PC.
In large chemical plants, a maintenance professional may spend half a day or more researching to perform work on an asset. That person may need to access a dozen systems to capture data required to perform the work. In some cases, that half a day could mean the failure of the asset.
Proper maintenance is not just about monitoring but also about data and document management that allows personnel to be more productive and proactive. One system that often has a gatekeeper or a professional well-trained in the software is an enterprise resource planning (ERP) system. There are numerous manufacturers of ERP systems, and all of them require firsthand training and knowledge to navigate within these systems.
The maintenance professional needs to know if the replacement parts for an asset are available and where they are located in the plant. If the parts are not in the plant, then getting them on order as quickly as possible is critical to prevent an asset failure or process downtime. These new systems are capable of tying into various ERP systems and extracting data that the maintenance professional can access on a mobile device or computer. As a result, this maintenance professional will not have to wait for an answer from the ERP master to determine if parts are available.
As new data-processing systems are deployed, a culture change may be necessary in the organization. For instance, maintenance personnel may receive a default signal from the system. The maintenance people visit the asset in trouble and find no elevated temperatures or noise from the asset and assume it was a false alarm. Then the asset becomes fully disabled at a later date. With new IIoT-capable sensors, default detection can be highly sensitive and accurate for detecting anomalies that can be fixed, eliminating downtime in many cases.
A large facility could have many different operating systems. Because of a lack of standardization in industrial control systems, these systems may not communicate with each other. As a result, a process upset in one portion of the plant can have significant downstream effects in another system of the plant. Technology has been developed that offers the capability to bridge the communication gap by capturing data from all systems and reporting a greater overall picture to stakeholders than stand-alone systems.
Figure 2. This IIoT-equipped pumping loop incorporates operation performance measures, future maintenance requirements and overall process measures.
The team can collect all data such as drawings and installation and maintenance manuals, as well as check on the availability and location of spare parts. The maintenance professional can access both augmented reality and educational videos that demonstrate the necessary maintenance from his or her smartphone, tablet or PC.