Examples of network topologies - A: star, B: mesh, C: hybrid star-mesh.
The Magic Behind Condition Based Maintenance
Bryan Christiansen, CEO of Limble CMMS, explains why Condition Based Maintenance (CBM) is becoming the methodology of choice for operators of manufacturing facilities.
Faced with generational shifts, tight budgets and reduced head count, today’s manufacturing organizations are forced to do more with less. One way they are bridging these gaps is by implementing Condition Based Maintenance (CBM), a method one study concluded may reduce maintenance expenditure on owned assets by as much as 75 per cent.
How does CBM work?
Condition Based Monitoring relies on remote sensors measuring the circumstances of assets in real-time. Simple examples are vibration on rotating equipment, analysis of contaminants in lubrication, or flow restriction though a heat exchanger. This data is fed into software that alerts the operation of potential issues occurring in the process.
A simple diagram of the data flow:
| Sensors | -> via radio frequency -> | Gateway | -> via internet (https) -> | CMMS |
Based on this information, the operation can get a picture of the overall health of their equipment. They can plan accordingly for upcoming problems, reducing unplanned downtime and cost to the operation.
Need more details? Read ahead to learn more about the magic behind CBM.
CBM starts with sensors in the field. There are many types of sensors available today, and the selection can be daunting. In general, you should select sensors that can detect the most common failure modes on your asset.
In certain environments, it can be prudent to build an industrial wireless network. This reduces the barrier for sensor procurement and installation, because wiring these devices can be costly. It also allows for flexibility in moving the sensors later if needed.
The sensors will need a power source. This can be wired, from a battery, or in some cases energy harvesting may be viable. The sensor will measure a signal, and in most cases, a transmitter will convert the signal into a small current (4-20 mA). The current signal is then read by a receiver, which will interpret the current into a measurement that makes sense to the operation.
In the case of wireless sensors, the converted signals are transmitted via a wireless protocol. There are many different protocols, but the most common ones are Wifi, 802.11, Bluetooth, Zigbee, and HART.
Selection of radio frequency is more nuanced than one may expect. Because most consumer electronics use Wi-Fi or Bluetooth which use the 2.4 GHz band, this frequency can become cluttered in an industrial setting. Lower frequencies can also travel longer and are less impeded by objects in the path of the signal. Therefore, many operations turn to lower frequencies to use for wireless signal carrying.
There are two main topologies in industrial wireless networks: star and mesh configurations. In the star topology, sensors feed into a sole gateway, or “hub”. These types of networks are easier to manage and can be scaled quickly. The downside is the reliance on one gateway. If something happens to the sole point where all the data flows through, the system is no longer functional. These systems can run into range issues over wide areas.
In a mesh configuration, each sensor acts as a node. The nodes can pass information to any other node in the network. Thus, losing one data point does not affect the network, because the signal will find the next least resistant path to the gateway. Redundant gateways are commonly commissioned to add robustness. The downside with the mesh topology is the added cost and complexity in installation, configuration, and maintenance. The power draw is higher on devices as well.
Gateways act as the collection point for the signals coming from the field, and act as the interface between the networks. They can transform the data into a structure that is understandable by the next system. The information is converted into data readable by end use software, such as the CMMS.
The gateway should be reachable by all devices in a star configuration. Where devices have trouble connecting to the gateway, signal boosters can be employed. In a mesh network, not all devices need to reach the gateway directly, as long as they can through other nodes.
Now that the data has passed through the gateway, it can be interfaced with the CMMS. This is where the CBM concept becomes truly powerful. A CMMS combined with a good sensor network can give you a very good picture of your asset health.
Some automation can be built into the system. For example, detections in sensor levels can feed into the CMMS (note that not all CMMS solutions support this). A sensor could have a certain alert level to recognize. Upon alert level, this software can be configured to automatically generate a work order.
Example of automatic task creations:
- Create a “Fill Oil” task when the level of oil in the reservoir gets too low.
- Create an inspection task when the temperature in a refrigeration room gets too high.
- Create a diagnostic maintenance task when a sensor detects increased vibrations on a gearbox.
- Create a preventive maintenance task when a machine has run for a certain amount of time or cycles.
These functions are enablers that can lead to more autonomy of the operation. The maintenance program can move into a predictive mode, where fluctuations in the sensor readings are understood as the beginning stages of equipment wear. Armed with this information the operation has the chance to plan properly for a potential failure - and work the repair into the upcoming schedule.
Trends can also be generated by the software, once enough information is available. The trends can be used to understand maintenance performance over time.
This setup can be replicated easily, depending on the operation. It could be as simple as mounting a few more sensors, connecting them to the network gateway, and setting thresholds on the data coming into software.
Though a CBM strategy can be expensive, time-intensive, and complex in implementation, the benefits can be well worth the initial effort. Benefits include reduced downtime, lower long-term maintenance spend, and better asset utilization. All these advantages contribute positively to the plant bottom line.
Through the "magic" of CBM, the data that is generated on the shop floor can be quickly analyzed and fed into business execution programs such as the CMMS. With the insight of the plant personnel, the data can be turned into information, which will lead to a more reliable and efficient operation.