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Capacitive, the Other Proximity Sensor

What is the first thing that comes to mind if someone says “proximity sensor?” My guess is the inductive sensor, and justly so because it is the most used sensor in automation today. There are other technologies that use the term proximity in describing the sensing mode, including diffuse or proximity photoelectric sensors that use the reflectivity of the object to change states and proximity mode of ultrasonic sensors that use high-frequency sound waves to detect objects. All these sensors detect objects that are in close proximity to the sensor without making physical contact. One of the most overlooked or forgotten proximity sensors on the market today is the capacitive sensor.

Capacitive sensors are suitable for solving numerous applications. These sensors can be used to detect objects, such as glass, wood, paper, plastic, or ceramic, regardless of material color, texture, or finish. The list goes on and on. Since capacitive sensors can detect virtually anything, they can detect levels of liquids including water, oil, glue, and so forth, and they can detect levels of solids like plastic granules, soap powder, sand, and just about anything else. Levels can be detected either directly, when the sensor touches the medium, or indirectly when it senses the medium through a non-metallic container wall.

Capacitive sensors overview

Like any other sensor, there are certain considerations to account for when applying capacitive, multipurpose sensors, including:

1 – Target

    • Capacitive sensors can detect virtually any material.
    • The target material’s dielectric constant determines the reduction factor of the sensor. Metal / Water > Wood > Plastic > Paper.
    • The target size must be equal to or larger than the sensor face.

2 – Sensing distance

    • The rated sensing distance, or what you see in a catalog, is based on a mild steel target that is the same size as the sensor face.
    • The effective sensing distance considers mounting, supply voltage, and temperature. It is adjusted by the integral potentiometer or other means.
    • Additional influences that affect the sensing distance are the sensor housing shape, sensor face size, and the mounting style of the sensor (flush, non-flush).

3 – Environment

    • Temperatures from 160 to 180°F require special considerations. The high-temperature version sensors should be used in applications above this value.
    • Wet or very humid applications can cause false positives if the dielectric strength of the target is low.
    • In most instances, dust or material buildup can be tuned out if the target dielectric is higher than the dust contamination.

4 – Mounting

    • Installing capacitive sensors is very similar to installing inductive sensors. Flush sensors can be installed flush to the surrounding material. The distance between the sensors is two times the diameter of the sensing distance.
    • Non-flush sensors must have a free area around the sensor at least one diameter of the sensor or the sensing distance.

5 – Connector

    • Quick disconnect – M8 or M12.
    • Potted cable.

6 – Sensor

    • The sensor sensing area or face must be smaller or equal to the target material.
    • Maximum sensing distance is measured on metal – reduction factor will influence all sensing distances.
    • Use flush versions to reduce the effects of the surrounding material. Some plastic sensors will have a reduced sensing range when embedded in metal. Use a flush stainless-steel body to get the full sensing range.

These are just a few things to keep in mind when applying capacitive sensors. There is not “a” capacitive sensor application – but there are many which can be solved cost-effectively and reliably with these sensors.

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Predictive Maintenance vs. Predictive Analytics, What’s the Difference?

With more and more customers getting onboard with IIoT applications in their plants, a new era of efficiency is lurking around the corner. Automation for maintenance is on the rise thanks to a shortage of qualified maintenance techs coinciding with a desire for more efficient maintenance, reduced downtime, and the inroads IT is making on the plant floor. Predictive Maintenance and Predictive Analytics are part of almost every conversation in manufacturing these days, and often the words are used interchangeably.

This blog is intended to make the clear distinction between these phrases and put into perspective the benefits that maintenance automation brings to the table for plant management and decision-makers, to ensure they can bring to their plants focused innovation and boost efficiencies throughout them.

Before we jump into the meat of the topic, let’s quickly review the earlier stages of the maintenance continuum.

Reactive and Preventative approaches

The Reactive and Preventative approaches are most commonly used in the maintenance continuum. With a Reactive approach, we basically run the machine or line until a failure occurs. This is the most efficient approach with the least downtime while the machine or line runs. Unfortunately, when the machine or line comes to a screeching stop, it presents us with the most costly of downtimes in terms of time wasted and the cost of machine repairs.

The Preventative approach calls for scheduled maintenance on the machine or line to avoid impending machine failures and reduce unplanned downtimes. Unfortunately, the Preventative maintenance strategy does not catch approximately 80% of machine failures. Of course, the Preventative approach is not a complete waste of time and money; regular tune-ups help the operations run smoother compared to the Reactive strategy.

Predictive Maintenance vs. Predictive Analytics

As more companies implement IIoT solutions, data has become exponentially more important to the way we automate machines and processes within a production plant, including maintenance processes. The idea behind Predictive Maintenance (PdM), aka condition-based maintenance, is that by frequently monitoring critical components of the machine, such as motors, pumps, or bearings, we can predict the impending failures of those components over time. Hence, we can prevent the failures by scheduling planned downtime to service machines or components in question. We take action based on predictive conditions or observations. The duration between the monitored condition and the action taken is much shorter here than in the Predictive Analytics approach.

Predictive Analytics, the next higher level on the maintenance continuum, refers to collecting the condition-based data over time, marrying it with expert knowledge of the system, and finally applying machine learning or artificial intelligence to predict the event or failure in the future. This can help avoid the failure altogether. Of course, it depends on the data sets we track, for how long, and how good our expert knowledge systems are.

So, the difference between Predictive Maintenance and Predictive Analytics, among other things, is the time between condition and action. In short, Predictive Maintenance is a stepping-stone to Predictive Analytics. Once in place, the system monitors and learns from the patterns to provide input on improving the system’s longevity and uptime. Predictive Maintenance or Preventative Maintenance does not add value in that respect.

While Preventative Maintenance and Predictive Maintenance promises shorter unplanned downtimes, Predictive Analytics promises avoidance of unplanned downtime and the reduction of planned downtime.

The first step to improving your plant floor OEE is with monitoring the conditions of the critical assets in the factory and collecting data regarding the failures.

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