What is a membrane switch?
A membrane switch is an operating system integrating key function, indicating element and instrument panel. The membrane switch, also called a membrane keyboard or membrane keypad, is a custom switch assembly that uses pressure to open or close the conducting path in an electrical circuit. The ultimate purpose of a membrane switch is to serve as the interface between man and machine, enabling an operator to communicate with a piece of equipment, instrument, or machinery. Membrane switches are still consistently utilized as user interfaces for a wide range of products from industrial machinery to medical devices to agricultural equipment. Even the most rigorous of industries can count on proven membrane switch technology to provide a reliable, long-lasting human-machine interface.
How about the construction of a membrane switch?
A membrane switch assembly differs from traditional mechanical switches. Membrane switches are essentially a sandwich of thin layers that are bonded together using pressure sensitive adhesives. The layers of a basic membrane switch construction include: a membrane overlay, spacer, printer circuit, rear adhesive, and tail filler. Other electronic components can be implemented, as well, such as discrete LEDs, backlighting sources, and connectors that make membrane switches easy to integrate into other systems or devices. Each layer in a membrane switch assembly serves a different purpose, and custom features require the addition of specialty layers. Depending on the environment and requirements of the application into which the membrane switch plugs into, the construction can vary.
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Deep learning is one of the most promising areas in artificial intelligence, and it has the potential to revolutionize control design and the industrial Internet of Things (IIoT). In the realm of industrial automation, machine vision plays a critical role, enabling data-driven deployment of complex machinery. As industries evolve, embedded design teams are increasingly turning to advanced technologies like machine learning (ML) and deep learning (DL) to develop and deploy sophisticated systems efficiently—despite limited resources.
By leveraging these technologies, teams can construct intricate single or multi-system models using data-driven approaches. Unlike traditional ML, which relies on physics-based models, DL algorithms learn system behavior directly from data. While conventional ML works well for small-scale problems, it struggles with large, complex datasets—such as those found in autonomous vehicles. This is where deep learning excels, offering powerful tools to tackle big data challenges. This article explores how this cutting-edge technology is shaping the future of control design and IIoT applications.
One of the key areas where ML is making a significant impact is in the monitoring of industrial assets. Machine learning helps transition from reactive and preventive maintenance to predictive maintenance, allowing early detection of anomalies and better estimation of remaining useful life for equipment like motors, pumps, and turbines.
The process of developing and deploying an ML-based model is illustrated in Figure 1. It begins with data collection from various sensors—such as accelerometers, thermocouples, and current sensors connected to a motor. The next step is feature engineering, which involves extracting meaningful information from raw sensor data and reducing redundancy through techniques like Principal Component Analysis (PCA). These features are then used as input for ML algorithms, which iteratively test different models to find the best fit for the application.
Figure 2 shows how the current signal from a motor can be analyzed using spectral analysis, where frequency bands provide insights into the motor’s condition. Feature extraction from multiple sensors may lead to redundant data, so methods like PCA help simplify the dataset. Once the features are refined, they are fed into the ML algorithm for training and validation.
Unsupervised learning techniques, such as Gaussian Mixture Models (GMM), can detect deviations from normal behavior without requiring labeled data. On the other hand, supervised learning algorithms, like Support Vector Machines (SVM) or Neural Networks, require labeled data to identify the root cause of anomalies.
However, traditional ML often faces challenges due to the labor-intensive nature of feature engineering. This is where deep learning shines, as it eliminates the need for manual feature extraction. Instead, raw sensor data can be directly input into a deep neural network, as shown in Figure 4.
Deep learning is built upon artificial neural networks, which are inspired by biological neural systems. These networks consist of layers of interconnected nodes, including an input layer, one or more hidden layers, and an output layer. Each layer processes the input data and passes it forward, adjusting weights during training to improve accuracy. This layered structure allows deep learning models to automatically learn relevant features from the data, a process known as feature learning.
As depicted in Figure 5, a feedforward neural network processes data in a sequential manner, with each layer influencing the next. This capability makes deep learning particularly effective in handling high-dimensional and complex datasets, paving the way for smarter, more adaptive industrial systems.