Simple sensing is great value

In a previous life I was a production manager. One of the frustrations of such a role is the feeling that more control and coordination could be exerted “if only I had the data for…”. Modern manufacturing plant often provides either local instrumentation, or the remote logging of its operational data, but if we want to think about integrating plant, and therefore think about optimising operations of the whole system, there is usually some basic information that is missing.

In the late 1990s, transducers for sensing were expensive, and the computational resource required to deal with the sensing data was similarly difficult to justify. This situation was also compounded by the general lack of network infrastructure, which was essentially pre-WiFi. Radio links were available, but they were a) costly and b) prone to interference, or had poor transmission range.

Fast forward to recent times, where:

• transducers are cheap;
• microprocessors are cheap, more than capable for signal conditioning and limited data storage, and easy to network;
• network availability is pervasive via, wired, WiFi, Bluetooth, 4G, etc.

What does this mean for the production/operations manager who still has the same answer of “if only I had …” when they are looking for ways to increase productivity?

It means that we are in an age where it is cheap to experiment with sensing, and it is cheap to integrate the sensing that may already exist, but which is not being used for holistic decision making.

There still exists the situation where manufacturing plant does not produce data while it operates, and it is incumbent upon operators to count items to record data about operations.

Let’s say that we want to gather some data from a production line. Products are produced and transported to a destination via a conveyor belt. We’ll assume that the products are identical, and that they all follow each other in single file. The production manager wants some indication of what is happening in realtime, plus a set of alerts when a significant event has occurred.

So, we fasten a light source and a photocell, or some sort of proximity detector to the side of the conveyor belt. What do we record?

In terms of data, we record a time and date stamp every time an object is detected. We assume that the conveyor belt moves only in one direction (it doesn’t reverse in some situations for instance), and that the sensor does not produce false positives (when it says that there is an object present, we can trust the statement).

As the production line operates, objects move along the conveyor and the sensor produces data in the form of a stream of time and date stamps, which might look like this:

03-09-2019 10:57:23

03-09-2019 10:57:29

03-09-2019 10:57:35

03-09-2019 10:57:41

Let’s assume that we have connected our sensor to a small microprocessor such as an Arduino board, or even a Raspberry Pi. That board will enable the incoming signal from the sensor to be augmented with a timestamp and written to a file, or sent via a network connection to a PC where the data is recorded.

What can we do with that data? With one sensor we can:

• count the total number of objects produced;
• measure the rate at which objects are produced;
• identify events which occur that might interrupt the flow (e.g. system breakdown, changeover between product type, etc.)

If we augmented the system with an additional sensor, placed either above or below the first sensor, we could also distinguish between two product types if each has a different height. But we shall keep to the simpler example of uniform products to keep it simple.

Now that we have the data, we can produce very simple reports of production output and rate, while also creating alerts for when the objects appear not to be arriving.

Furthermore, the data is now being captured and stored in a way that can now be synthesised with other such systems – adding more sensing to other parts of the plant will now enable a more holistic view of the operations to be created.

These first, tentative steps towards data capture are an important introduction to the modernisation of industry through digital manufacturing (or Industry 4.0). Whilst the latest examples of integrated manufacturing plants illustrate the possibilities of Cyber Physical Systems to coordinate, control and actuate physical systems on our behalf, there is still a fundamental reliance on the generation of data via sensing, its collection, processing, and subsequent reporting in a manner that humans can comprehend.