Alberto Diez-Olivan

Tecnalia · Industry and Transport Division

Predictive maintenance is fully implemented in the oil and gas industry, and the impressive development of field sensors, big data, and digital twins offers a wide field for the ongoing experimentation and development of diagnostic and prognostic tools for machinery. Although a wide range of technologies and sensors is available, vibration analysis...

Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of...

Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things (IoT) sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of non-stationary phenomena that affects the data collected over time. Consequently, fault patterns learned from d...

The Industry 4.0 revolution allows monitoring and intelligent processing of big amounts of data. When monitoring certain assets, very few data is found for operation under faulty conditions because the cost of not operating properly is unacceptable and thus preventive strategies are put in practice. Because machine learning algorithms are data exha...

This paper proposes a hybrid model (HyM) for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train. This HyM fuses data from two sources: data taken from the real system and synthetic data generated using a physics-based model of the HVAC. The physical model of the HVAC was developed to include the sensors locate...

Quality control in manufacturing is a recurrent topic as the ultimate goals are to produce high quality products with less cost. Mostly, the problems related to manufacturing processes are addressed focusing on the process itself putting aside other operations that belong to the part’s history. This research work presents a Machine Learning-based a...

The Industry 4.0 revolution allows gathering big amounts of data that are used to train and deploy Artificial Intelligence algorithms to solve complex industrial problems, optimally and automatically. From those, Long-Short Term Memory Fully Convolutional Network (LSTM-FCN) networks are gaining a lot of attention over the last decade due to their c...

Technological developments in the manufacturing industry have triggered out the capacity of acquisition, storage and processing of the data. Mostly, the problems related to manufacturing processes are addressed focusing on the process itself, putting aside other operations that belong to the part’s history. Blind fasteners are of special interest f...

In many complex industrial scenarios where condition monitoring data are involved, data-driven models can highly support maintenance tasks and improve assets’ performance. To infer physical meaningful models that accurately characterize assets’ behaviors across a wide range of operating conditions is a difficult issue. Usually, data-driven models a...

Blind fasteners are of special interest for aircraft construction since they allow working on joints where only one side is accessible, as is the case in many common aerospace box-type structures, such as stabilizers and flaps. This paper aims to deliver an online monitoring method for the detection of incorrect installed blind fasteners. In this t...

The so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift...

This paper presents a novel algorithm to assess the health status in monitoring sensor data using a kernel-based support vector machine (SVM) approach. Today, accurate fault prediction is a key issue raised by maintenance. In particular, automatically modelling the normal behaviour from condition monitoring data is probably one of the most challeng...

An efficient and sustainable animal production requires fine-tuning and control of all the parameters involved. But this is not a simple task. Animal farming is a complex biological system in which environmental parameters and management practices interact in a dynamic way. In addition, the typical non-linear response of biological processes implie...

Knowledge extraction from monitoring sensor data has gained a lot of attention from many fields of research during recent years. Artificial intelligence, machine learning, advanced statistics, the Internet of things and architectures and strategies for optimal big data management are good examples of such interest. This is mainly due to the increas...

In this work we introduce a kernel density-based pattern classification approach for the automatic identification of behavioral patterns from monitoring data related to blind fasteners installation. High density regions are estimated from feature space to establish behavioral patterns, automatically removing outliers and noisy instances in an itera...

Today, failure modes characterization and early detection is a key issue in complex assets. This is due to the negative impact of corrective operations and the conservative strategies usually put in practice, focused on preventive maintenance. In this paper anomaly detection issue is addressed in new monitoring sensor data by characterizing and mod...

Structural health monitoring is a process for identifying damage in civil infrastructures using sensing system. It has been increasingly employed due to advances in sensing technologies and data analytic using machine learning. A common problem within this scenario is that limited data of real structural faults are available. Therefore, unsupervise...

Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each al...

This paper presents an application tool developed to support effective maintenance in the railway sector. Considering that this is a sector where maintenance is especially critical, due to factors such as urgency in corrective maintenance operations, integration of hundred of subsystems and integration of different sources of information, this make...

This paper presents an application tool developed to support effective maintenance in the railway sector. Considering that this is a sector where maintenance is especially critical, due to factors such as urgency in corrective maintenance operations, integration of hundred of subsystems and integration of different sources of information, this make...

En este trabajo se presentan los resultados obtenidos tras la realización de diversas pruebas en un dominio ferroviario real. Estos resultados muestran cómo el sistema evolutivo desarrollado obtiene de forma automática el conocimiento basado en reglas difusas y cómo éste permite la detección de estados anómalos del tren.

The success of intelligent diagnosis systems normally depends on the knowledge about the failures present on monitored systems. This knowledge can be modelled in several ways, such as by means of rules or probabilistic models. These models are validated by checking the system output fit to the input in a supervised way. However, when there is no su...

Classical approaches when building diagnosis and monitoring systems are rule-based systems, which allow the representation of existing knowledge by using rules. There are several techniques that facilitate this task, such as fuzzy logic, which allows knowledge to be modeled in an intuitive way. Nevertheless, sometimes it is not easy to define the f...