Today's industrial automation market continues to be driven by a complete-solution focus, with emphasis on reduced cost of ownership and increased value for the user. Increasingly, though, users turn to subsystems to save commissioning time and reduce costs.

In this sixth essay of my series on selecting sensors, we get to the real meat and potatoes of the job—writing a workable specification. If you've been following each step of the process outlined in the series, you realize you can't just pick a product out of a catalog.

The hope of every manufacturing management team is to deliver defect-free product. But if that isn't feasible, then the next goal is to minimize defects as much as possible. That's where error proofing comes into play. While it in no way accepts even a small number of defects, its two-pronged approach aims to either prevent them from ever happening or to catch them as soon as possible. And the primary weapons in error proofing's arsenal are sensors.

This is the fifth essay in a series expanding on an article I wrote for Sensors titled "A Twelve-Step Sensor Selection Checklist". This month, I'll look at the phase of the sensor selection process in which you review your error analysis. In this step, you decide if a particular sensor meets your measurement requirements. Some people balk at doing detailed analysis, saying it's unnecessary. Others will want to just try a sensor and see how it works. In most cases, the analysis approach beats the cut-and-try method hands down. You've come this far. Don't waste the effort.

This is the fourth essay in a series expanding on an article I wrote for Sensors titled "A Twelve-Step Sensor Selection Checklist". In October, I discussed the need to consider factors that influence measurement conditions. This month, I'll talk about other considerations and best practices for making a list of sensor candidates.

Current economic conditions, global competition, and delays of new equipment purchases are causing manufacturers to be sensitive about all aspects of operational costs. In this environment, it pays to consider both creative and proven methods that you can use to bring your product to market at minimum cost. Overall Equipment Effectiveness (OEE) is a method that meets this objective.

This is the third essay in a series expanding on an article I wrote for Sensors titled "A Twelve-Step Sensor Selection Checklist". This month's piece deals with the factors that influence measurement and calibration uncertainty.

Supervisory control and data acquisition (SCADA) systems monitor and control remote or local industrial equipment and processes in real time. Many of these systems have been in place for years, with little attention paid to who has access to them and how vulnerable they may be to deliberate or inadvertent compromise.

In my last essay, I began a series on the step-by-step process careful engineers often follow to select a suitable sensor for an application. In that essay, I explained how to establish measurement span, or range, requirements. This month, I'll look at accuracy and measurement uncertainty.

Today, sensors are everywhere. They do everything from counting parts on an assembly line to measuring the quality of products. But some of the biggest challenges occur after measurements have been made. At that point, you have to decide: Where do I collect the data, and how can I use it to improve my operations by decreasing variability and improving quality?