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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). Machine vision plays a crucial role in industrial automation, enabling data-driven deployment of complex systems. As industries move toward smarter and more autonomous operations, the integration of AI technologies like machine learning (ML) and deep learning (DL) becomes essential.
Today’s embedded design teams are constantly seeking innovative solutions to meet customer demands efficiently. With limited resources, they turn to ML and DL to develop advanced systems on time. These technologies allow teams to build complex single or multi-system models using data-driven approaches. Unlike traditional physics-based models, ML and DL algorithms learn system behaviors directly from data. While traditional 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 shines, offering powerful tools to tackle big data challenges.
This article explores how these emerging technologies are shaping the future of control design and IIoT applications. One key area is the use of ML in industrial asset condition monitoring. ML transforms maintenance strategies from reactive and preventive approaches to predictive ones, allowing early detection of anomalies and estimation of remaining useful life for critical equipment like motors, pumps, and turbines.
The ML workflow typically involves data collection, feature engineering, model training, and validation. Sensors such as accelerometers, thermocouples, and current sensors gather real-time data, which is then processed through feature extraction and reduction techniques like Principal Component Analysis (PCA). The resulting features are used to train ML models, which can detect deviations from normal behavior. For instance, analyzing the frequency spectrum of a motor’s current signal can reveal signs of wear or failure.
Unsupervised learning methods, such as Gaussian Mixture Models (GMM), are particularly useful for identifying abnormal patterns without labeled data. On the other hand, supervised learning algorithms like Support Vector Machines (SVM) and neural networks are used to classify faults based on labeled datasets. However, traditional ML relies heavily on manual feature engineering, which is time-consuming and requires domain expertise.
This is where deep learning offers a significant advantage. By leveraging artificial neural networks, DL automatically learns features from raw sensor data without the need for extensive preprocessing. A typical deep learning workflow includes input layers, hidden layers, and output layers, where each layer processes information and adjusts weights to improve accuracy. This hierarchical structure enables the model to extract high-level features from unstructured data, making it ideal for complex industrial applications.
As the industrial landscape continues to evolve, the adoption of deep learning will play a vital role in driving innovation, improving efficiency, and enhancing decision-making across various sectors.