# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
### Application of Machine Learning in the Industry  
Deep learning (DL) is a subset of ML methods that aim to model data with complex architectures that combine different non-linear transformations. The fundamental components of DL models are artificial neural networks (ANNs), which combine to form DNNs. These techniques have enabled significant progress in the fields of image processing [9; 10], pattern recognition [11] including facial recognition [12], speech recognition [13], computer vision [14], automated language processing [15], text classification [16], and recently, in the link quality estimation for a WSN. The fundamental characteristic of a DNN is the integration of multiple sequential layers, each made of multiple artificial neurons that require proper initialization and optimization algorithms for optimal performance. The right choice of DNN architecture can lead to results with outstanding performance compared to other methods [17]. In addition, the application of a multilayer ANN was shown to achieve better performance in pattern recognition in complex and highly variable environments [18]. Within the industrial sector, the multilayer perceptron (MLP) architecture is the one with the highest number of application cases partly due to its simplicity, which allows its implementation in devices with limited computational resources, such as those commonly found in the industry in developing countries [19]. The MLP has a basic architecture, where each unit or neuron in one layer is linked to all units in the next layer (fully connected) but has no link with neurons in the same layer. The number of hidden layers, the number of neurons in each layer, and the activation function define the architecture of the MLP model.  
The DL paradigm has been used successfully in classification problems; however, neural networks with many layers are sensitive to weight initialization and premature learning stoppage as the gradient values decrease towards small values, the phenomenon which is known as the vanishing gradient problem [20]. To ameliorate this problem, we may use the rectified linear unit (ReLU) activation function which has a maximum gradient value of 1, without saturating, allowing the network to continue learning. For this reason, in the present paper, the ReLU function was used as the activation function of the MLP model.  
With the continuous increase in device connectivity and computing capabilities of digital systems, some industries have succeeded in incorporating new methods based on artificial intelligence (AI). Hence, data provided by a wide variety of sensors are used to detect and prevent a possible failure or breakdown, especially via machine and process supervision systems. Indeed, failure detection is critical in the industry because it allows determining if it is necessary to stop a process and carry out maintenance actions [21]. [PERSON] et al. [22] use a DL model with long short-term memory (LSTM) in a failure detection case. They reported that the neural network was more sensitive to hyper-parameters such as batch size or the learning rate regarding the number of layers, which increased until no improvement was found.