When someone asks what we do at Wineman Technology (WTI) it is often hard to explain. The simplest answer is we build test equipment; although this statement is true, it fails to convey the most valuable service we provide to our customers. Test equipment describes a wide range of machines and hand-held devices performing design validation, quality control, end-of-line testing, and diagnostics. A wide variety of products use these machines and devices. Test equipment ranges from simple to complex and is in R&D labs, factory floors, and local service departments. How can we describe the niche we have carved out in this market and capture the unique value we bring? When I put this question to the engineering managers at WTI, one of them came back with the phrase “we provide insight through electromechanical test data”, and when he said it we knew we had defined what is special about WTI.
We have always said we have four key focus areas:
- Rotating media including dynamometers and linear actuators
- Hydraulic equipment (pumps, actuators, valves, and complex systems)
- Electronic test (hardware-in-the-loop (HIL) design validation and verification)
- Custom systems combining two or more of these, or so unusual they defy categorization
What ties these applications together, and why these applications? The answer is testing these items requires a combination of mechanical, electrical, and software engineering many other areas do not. But more than that: the idea of building a system to exercise a component over its entire operational range and helping developers or manufacturers better understand their product through storytelling with technical data excites us.
Storytelling can range from simple data visualization to advanced statistical modeling. One company asked WTI to generate certain complex reports requiring detailed calculations. The customer did not determine all these calculations, so WTI needed to understand the customer’s data and develop the best way to extract the desired insight. The result was a spreadsheet template into which the data acquisition software inserted raw measurements. This template used more advanced functions such as INDIRECT, MATCH, INDEX, and LINEST to implement a model. This is one example of applying the principles of data science in systems integration; WTI can also implement powerful machine learning algorithms such as logistic classifiers, neural networks, random forests, etc.
Designing and perfecting testers that can perform complex control algorithms, sequence through test procedures, collect data, and help the end user make decisions about their products requires a good understanding of electromechanical systems, software including data science, and the product. Our approach is to design the tester as a system with the customer’s part, the heart and soul of which is the software to make everything work. Within this model the entire project team needs to understand the big picture and work together in a way that allows something exceptionally important to happen.
This is why we rarely hire a person with a computer science degree to develop code for us, instead we seek out curious engineers who will understand how system components work together. Most importantly, in the end it is the presentation of product data that matters to our customers, which is why we endeavor to guide the engineering process early by helping our customers ask big product and organizational questions.