What Is Standing Between You And Your Results?

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With more complex tasks and more data to analyse the need to simplify and streamline has never been greater. Four milestones for simplifying common tasks across your applications will be discussed. By Jonah Paul, Senior Software Product Manager, NI

Engineers in every corner of the world are tasked with a straightforward yet incredibly complex challenge: solving the unsolvable. As if that were not enough, they are also expected to solve the unsolvable faster and with fewer resources than ever before. For example, consider testing an in-home thermostat. Gone are the days of a thermostat with just a bimetallic coil. Today’s thermostat has a convergence of technology, from humidity and temperature sensors to wireless circuitry and motion sensing. The validation of this system requires a wide array of instruments, sensors, and software expertise, but where should an engineer begin?

When faced with climbing a mountain that seemingly can not be scaled, engineers may find mapping out the steps they need to get to the summit challenging. Likewise, in engineering, trying to figure out the path of least resistance to measure and test a complex system can be daunting. Ultimately, the efficiency gained within each step adds up to the totality of the climb.

Now translate that to your engineering task list. By simplifying the common tasks across your applications, you can reduce the total time you need to develop, deploy, and manage your engineering systems. In the proverbial climb to the top of the mountain for engineering systems, the four fundamental milestones are implementing core concepts, setting up the system, analysing data, and designing for the unknown.

1.Implement Core Engineering Concepts

From the Nyquist sampling theory to the coefficients of a Proportional Integral Derivative (PID) algorithm, accurately implementing foundational engineering concepts in your application is critical. But nowadays, knowledge of core engineering concepts is not enough. You must implement these concepts within the semantics of the tools and the programming constructs you are using to create custom logic.

2.Set Up Your Measurement System
With a wide range of vendors developing instrumentation and sensors, you may find identifying and configuring the collective components in your system challenging. For example, consider the setup of a Histoindex Pte Ltd system, which was used to build a laser-based microscope for analysing unstained biopsy samples. This PC-based system relied on five different subsystems containing four different instrument types, including a galvanometer, laser, and cameras. With a system like this, basic tasks such as discovering the hardware, installing the right driver, and configuring and verifying the setup can be frustratingly time-consuming.

3.Analyse Measurement Data

In the past, characterising systems involved measuring only a few signals of similar sensor types, which provided more time for the subsequent analysis and report generation. Today, however, the number and diversity of the physical measurements you need to capture have dramatically increased.

4.Design for the Unknown

Though projects may begin with the promise of being common or routine, they can often evolve into something much more complicated. Engineers at Muc-Off Ltd experienced this firsthand as they built their measurement fixture for optimising performance lubricants. The team needed to adjust the rig to deal with different sprockets, chain types, gear ratios, power expectancies, and speeds. Early iterations of the rig used fixed functionality measurement devices for data acquisition and control; however, this system struggled to keep up and simply did not have the customisability and hardware diversity to do the job in the tight timeframes required.

Get Better Results

Change is constant in engineering, and results help to drive faster, more innovative solutions. The only way you can embrace this faster pace is to address it with a different approach — one that is tailored to engineers and scientists so they can focus on results. It is an approach that gives you, today’s engineer, instant insight and the ability to rapidly iterate and speed past the obstacles that slow others down.

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