## 2 Artificial Neural Network Models Background, and Fundamental Capabilities  
Seminal works in this ANN area were developed by [PERSON] and [PERSON] (1943) [7]; since then, the interest in ANN properties increased intensively, and after the publication of the [PERSON]'s book (1985) [8] and the development of backpropagation ANN models by [PERSON] and [PERSON] in 1986 [9], the interest was more focused on particular applications in industry (see Table 1). Following those initial works related to ANN models, different contributions were published for different purposes. The following references can be considered a good sample of works that can be found in the literature up to 1990, cataloged in Table 1, according to the reason for their utilization: association, classification, conceptualization, prediction, optimization, and filtering. * Association: Technique to reduce data dimensionality. * Classification: Technique for grouping data into classes. * Conceptualization: Technique for conceptualizing ideas based on concrete data. * Prediction: Technique to find values that are going to happen. * Optimization: Technique to seek convergence to a minimum. * Filtering: Technique for sifting data according to restrictions. ANN models allow us to obtain updated assets' condition analysis, and according to the status of the environment variables, they are able to predict production, adding capabilities to foresee existing and potential problems (fault, failure, production losses, etc.) based on collected information from sensors in each particular asset (with an approach very similar to current studies on the Internet of Things--IoT) [30; 31; 32]. ANN models are mathematical tools emulating human reasoning, learning from past experiences and coping with rather complex non-linear behaviors [33]. These models are especially well suited to replicate certain behavioral patterns where relationship among input and output variables cannot be explained by other mathematical techniques [34; 35]. Therefore, we can conclude that ANN models have as their main advantages the capacity to find complex relations among variables, with a high tolerance to data uncertainty (thanks to redundancy in data storage), and providing predicted variable patterns in-real time [36; 37; 35]. Also, we can say that ANN models have as their main disadvantages the need for abundant information, with enough data quality, which is not always accessible or available at a reasonable cost [38]. ANNs are built with the mission of processing the information of inputs and transferring that information through different connections where it is activated by a transference function, which is tuned up using a training process that can be developed when reasonable real data are provided [39]. In this process, ANNs are usually trained with 75% of available data (training set) and with the remaining data, 25%, the network is validated (test set) [39; 40]. There are different proposed architectures in the literature for ANNs (feed-forward, sequential, convolutional,... ), but in its simplest form, a feed-forward neural network, also known as a perceptron, consists of several interconnected layers of processing units called neurons [34; 41] as shown in Figure 1. The first layer, called the input layer, is composed of P neurons, arranged as a P-dimensional vector; the intermediate layer, called the hidden