Simplifying connectivity for predictive maintenance

Predictive maintenance aims to predict when machine failure might occur and to prevent its occurrence by performing maintenance. Not only does predictive maintains ensure reliability, but it brings huge cost savings in comparison with routine or scheduled preventative maintenance, because tasks are performed only when needed.

For many businesses, however, implementing predictive maintenance has been disappointingly slow out of the starting blocks. In fact, implementing predictive maintenance has been harder than expected; making it a testing challenge to obtain valuable insights from collected data. But by now, we have come to learn that with new opportunities come new challenges, and enabling predictive maintenance has not been the exception. The two main challenges that managers have to deal with are performing diverse data acquisition and deploying edge intelligence. For the purpose of this article, we discuss how to address diverse data acquisition.

Too many connections

The beauty of predictive maintenance lies in the unlocking of valuable insights from diverse sets of data to prevent downtime. Hence, manufacturers need to acquire volumes of new data by adding more sensors and devices to legacy machines for analysis and intelligence software development in the cloud or IT systems. On paper, everything seems straightforward, but the problem comes in that the interfaces and protocols of legacy machines are varied. For example, barcode scanners and displays use RS-232, RS-422, or RS-485 serial interfaces while tower lights and signal lights use analog and digital I/O interfaces, respectively. What’s more, industrial standard protocols, such as Modbus, EtherNet/IP and PROFIBUS, need to be used simultaneously in a network.

Commonly, most businesses tend to just use familiar PLCs or communication modules to connect devices of different proprietary protocols as well as different analog and digital I/O interfaces, installing edge gateways to convert industrial standard protocols to online SCADA, cloud, or IT systems in the absence of cloud communication abilities.

However, we know that PLCs are assigned for control jobs such as storing procedures, sequential or position control, time counting, and input or output control. The additional data acquisition and edge-to-cloud protocol conversion jobs maybe minor in the case of just one or two points, but will bring major challenges to the fore in managing diverse connectivity on a large scale.

The issues

A closer look at the challenge at hand shows that three issues are complicating things to get predictive maintenance out of the starting blocks:

• The Costs – The need for additional PLC modules for protocol converters and I/Os can be prohibitively expensive. In addition, legacy PLC may not have the capability to communicate in cloud protocols such as MQTT or AMQP.
• Time-consuming – Extra configuration and programming efforts are required to realize edge-to-cloud connectivity from scratch, and they have often proven to be time-consuming in large deployments.
• Troubleshooting – Troubleshooting can also add to engineers’ frustration as it is very difficult and time-consuming to pinpoint all the communication issues caused by incorrect software parameters, such as slave IDs and register addresses, or incorrect command configurations in large-scale networks.

Simplifying diverse data acquisition

Even when so many protocols and interfaces are involved, system integrators ideally want to keep everything about connectivity in a network sweet and simple, especially when it comes to data acquisition from multiple, diverse sources. For them to keep things as simple as possible, seeking out ready-to-run connectivity offerings that easily convert protocols of multiple field devices, including serial, I/O, Modbus, and EtherNet/IP, to MQTT, is strongly advised. MQTT is the most widely used protocol for cloud connectivity and enables communication with intelligent software and systems in private and public cloud platforms, such as Azure and Alibaba Cloud.

Other key considerations for connectivity must include diagnostics tools that ensure the accuracy of settings and a connection loss buffer function that avoids packet loss when the cloud connection gets disconnected.
With regard to troubleshooting issues, devices’ troubleshooting tools are definite deal breakers. Embedded traffic monitoring and diagnostic information functions, along with a convenient web console, make troubleshooting easy.