Biometrics done wrong
In the tech industry, we are used to buzzwords sweeping through the media and general cultural awareness that had a very narrow technical origin. It gets complicated if that meaning gets lost in translation, but the hype remains. “Biometrics” has started to take on that form. Let us unpack it a bit and see where […]
Web E2E software development testing with Cypress & Cucumber
Background End-to-end (e2e) testing is a method of software development testing that validates user flows and behaviour. With this testing methodology, we can simulate and automate a suite of tests that perform actions as our users would. When dealing with large production systems, you will quickly see that exclusively performing manual exploratory testing results in […]
Software development best practice: Writing clean code – Part 2
In the first part of this series I wrote code for a very simple use case that violates some of the best practices for writing clean code. In this second part, I’m going to rewrite the code using the Factory Pattern and we are going to look at why this code is much better. Keep […]
Anomaly detection in predictive and preventive maintenance
It’s a given that machines will break (often when you least expect it) and is probably the oldest rule in manufacturing. Reliability and predictability are therefore critical elements in asset-intensive industries that use rotating machinery and industrial equipment. Downtime in these industries can result in losses to the tune of millions of rands. Despite this, […]
South African business are benefiting from reduced equipment breakdowns and maintenance cost with end-to-end predictive maintenance
Predictive maintenance systems are designed to help anticipate equipment failures so that corrective maintenance can be scheduled in advance. This approach can prevent unexpected machinery and equipment downtime, reduce maintenance costs and improve service quality for customers. It can also reduce any additional costs caused by over-maintenance in preventative maintenance policies. In this article we […]
4 Advantages of predictive maintenance on rotating machinery
Rotating machinery is a particularly fertile area for predictive maintenance and can have a significant positive impact on manufacturing and production efficiency. According to research conducted by Capgemini, almost 30% of artificial intelligence (AI) implementations in manufacturing are connected with machinery and production tool maintenance. This makes predictive maintenance the most broadly used use case […]
From fixing to predicting: An Introduction to Predictive Maintenance
Companies often maintain their assets reactively and repair or replace an asset only after it has already broken down. This is known as reactive maintenance. Reactive maintenance is problematic and is frequently associated with equipment breakdowns, which disrupts operations and leads to substantial losses. Reactive maintenance also leads to, amongst others, sub-optimal overall equipment efficiency. […]
5 Key factors to consider when developing a medical (IoMT) device
Despite economic uncertainties due to the COVID-19 pandemic, the medical technology industry (MedTech), according to Reportlinker, is expected to reach an estimated $432.6 billion by 2025. In this regard, the focus is being placed on interconnected devices as the healthcare industry is increasingly adopting connected devices intending to improve future healthcare altogether. It is advisable, […]
Key takeaways and trends: Internet of Things (IoT) World 2020 Expo
The IoT ecosystem is rapidly increasing, with reports estimating that by 2021 the number of connected devices is expected to reach 28 billion worldwide. This makes IoT-focused events such as the IoT World 2020 expo the ideal space to collaborate, discuss the latest trends and developments and test new products. Polymorph attended this virtual world […]
Why quality IoT data is critical for predictive maintenance and how it can be achieved
Our previous post concluded with the following statement: “Garbage in, garbage out” and highlighted that for predictive maintenance to be effective, you need refined, quality data. When data is “dirty” or fragmented you will not only have to spend a significant amount of time to turn it into useful information, but you will not be […]
