Diego Tibaduiza

Structural damage detection using principal component analysis and damage indices

One of the most important tasks in structural health monitoring corresponds to damage detection. In this task, the existence of damage should be determined. In the literature, several potentially useful techniques for damage detection can be found, and their applicability to a particular situation depends on the size of the critical damages that are admissible in the structure. Almost all of these techniques follow the same general procedure: the structure is excited using actuators, and the dynamical response is sensed at different locations throughout the structure. Any damage will change this vibrational response. The state of the structure is diagnosed by means of the processing of these data. Several studies have shown that the detection of changes in a structure depends on the distance from the damage to the actuator as well as the configuration of the sensor network. In this article, the authors considered the advantage of using an active piezoelectric system, where the lead zirconate titanate transducers are used as actuator and sensors in different actuation phases. In each actuation phase of the diagnosis procedure, one lead zirconate titanate transducer is used as actuator (a known electrical signal is applied), and the others are used as sensors (collecting the wave propagated through the structure at different points). An initial baseline model for undamaged structure is built applying principal component analysis to the data collected by several experiments and after the current structure (damaged or not) is subjected to the same experiments, and the collected data are projected into the principal component analysis models. Two of these projections and four damage indices (T 2-statistic, Q-statistic, combined index, and I 2 index) by each actuation phase are used to determine the presence of damages and to distinguish between them. These indices are calculated based on the analysis of the residual data matrix to represent the variability of the data projected within the residual subspace and the new space of the principal components. To validate the approach, data from two aeronautical structures—an aircraft skin panel and an aircraft turbine blade—are used.

Data-driven methodology to detect and classify structural changes under temperature variations

This paper presents a methodology for the detection and classification of structural changes under different temperature scenarios using a statistical data-driven modelling approach by means of a distributed piezoelectric active sensor network at different actuation phases. An initial baseline pattern for each actuation phase for the healthy structure is built by applying multiway principal component analysis (MPCA) to wavelet approximation coefficients calculated using the discrete wavelet transform (DWT) from ultrasonic signals which are collected during several experiments. In addition, experiments are performed with the structure in different states (simulated damages), pre-processed and projected into the different baseline patterns for each actuator. Some of these projections and squared prediction errors (SPE) are used as input feature vectors to a self-organizing map (SOM), which is trained and validated in order to build a final pattern with the aim of providing an insight into the classified states. The methodology is tested using ultrasonic signals collected from an aluminium plate and a stiffened composite panel. Results show that all the simulated states are successfully classified no matter what the kind of damage or the temperature is in both structures.

A Bioinspired Methodology Based on an Artificial Immune System for Damage Detection in Structural Health Monitoring

Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed.

Detection and classification of structural changes using artificial immune systems and fuzzy clustering

Among all the elements that are integrated into a structural health monitoring (SHM) system, methods or strategies for damage detection and classification are nowadays playing a key role in enhancing the operational reliability of critical structures in several industrial sectors. The main contribution of this paper is the application of a new methodology to detect and classify structural changes. The methodology is based on: 1) an artificial immune system (AIS) and the notion of affinity is used for the sake of damage detection; 2) a fuzzy c-means algorithm is used for damage classification. One of the advantages of the proposed methodology is the fact that to develop and validate the strategy, a model is not needed. Additionally, and in contrast to standard Lamb waves-based methods, there is no need to directly analyse the complex time-domain traces containing overlapping, multimodal and frequency dispersive wave propagation that distorts the signals and difficult the analysis. The proposed methodology is applied to data coming from two sections of an aircraft skin panel. The results indicate that the proposed methodology is able to accurately detect damage as well as classify those damages.

A sensor fault detection methodology applied to piezoelectric active systems in structural health monitoring applications

Damage detection is the basis of the damage identification task in Structural Health Monitoring. A good damage detection process can ensure the adequate work of a SHM System because allows to know early information about the presence of a damage in a structure under evaluation. However this process is based on the premise that all sensors are well installed and they are working properly, however, it is not true all the time. Problems such as debonding, cuts and the use of the sensors under different environmental and operational conditions result in changes in the vibrational response and a bad functioning in the SHM system. As a contribution to evaluate the state of the sensors in a SHM system, this paper describes a methodology for sensor fault detection in a piezoelectric active system. The methodology involves the use of PCA for multivariate analysis and some damage indices as pattern recognition technique and is tested in a blade from a wind turbine where different scenarios are evaluated including sensor cuts and debonding.

Proposal of Novel Model for a 2 DOF Exoskeleton for Lower-Limb Rehabilitation

Nowadays, engineering is working side by side with medical sciences to design and create devices which could help to improve medical processes. Physiotherapy is one of the areas of medicine in which engineering is working. There, several devices aimed to enhance and assist therapies are being studied and developed. Mechanics and electronics engineering together with physiotherapy are developing exoskeletons, which are electromechanical devices attached to limbs which could help the user to move or correct the movement of the given limbs, providing automatic therapies with flexible and configurable programs to improve the autonomy and fit the needs of each patient. Exoskeletons can enhance the effectiveness of physiotherapy and reduce patient rehabilitation time. As a contribution, this paper proposes a dynamic model for two degrees of freedom (2 DOF) leg exoskeleton acting over the knee and ankle to treat people with partial disability in lower limbs. This model has the advantage that it can be adapted for any person using the variables of mass and height, converting it into a flexible alternative for calculating the exoskeleton dynamics very quickly and adapting them easily for a child’s or young adult’s body. In addition, this paper includes the linearization of the model and an analysis of its respective observability and controllability, as preliminary study for control strategies applications.

Detection and characterization of defects in moving parts of wind turbines

The detection, localization and characterization of defects in a material or a part that conform a structure is possible by using the transmission and reception of ultrasonic signals. Different strategies are used to achieve extract information from the part under evaluation. For this, it is then possible to use a distributed sensors arrays on the surface of the material and using scanning techniques such as are A-scan or B-scan, where it is possible to increase the level of detail regarding location, orientation and size of defects found, according to the strategy used. However, the systems and inspection techniques are often limited by the geometries and access to different types of structures. Due to these reasons, the acquisition of the returned signals, for identification and attenuation time, can suppress valuable information for accurate characterization of imperfections found in shape and location. In this paper, the use of spectral analysis of the collected signals is proposed as a tool for detection and characterization of defects in a structure. This analysis allows to determining the power distribution in a frequency range. This methodology is useful in non-destructive evaluation when it is not possible to have full access to the structure under inspection. In this case it is applied on a wind turbine operating to make the study of different echoes captured according to the geometry of the part and comparing said conducting analysis with previously established patterns of shapes, orientations, and sizes of defects found.

Design and control of an exoskeleton in rehabilitation tasks for lower limb

According to statistical data provided by the National Administrative Department of Statistics (DANE), during 2005, 29.32% of Colombias disabled population had problems with their legs related to moving or walking. In order to contribute and help these people, medical science and engineering have been working together to provide solutions that can improve the quality of life of injured people. In this sense, it is possible to design and implement electromechanical devices to assist and facilitate movements in rehabilitation processes. Such devices can keep detailed records of movements performed, patient history, speed, force, muscle interaction, among others; also allows physiotherapists to have complete control of rehabilitation processes, improving it and offering a lot more possible treatments by analyzing the summarized collected data. Rehabilitation focused exoskeletons work as guide and support in physical therapy, those make sure that the treated patient performs correctly all its exercises, in case that the patient cannot perform the movement by himself such devices helps the patient to finish the exercise, improving the effectiveness of the therapy and reducing the time it takes to recover lost faculties. As a contribution to rehabilitation processes, this paper proposes the design of an exoskeleton by considering biome-chanical models involving the most possible characteristics of the human body to reduce the differences between the mathematical model and the real behavior of the body segments; That proposed model is used to constraint and design a controller in master-slave configuration to assist and ensure soft movements improving rehabilitation processes involving flection and extension movements in the sagittal plane of the lower limbs.

An acousto-ultrasonics pattern recognition approach for damage detection in wind turbine structures

The design of a Structural Health Monitoring (SHM) systems is a requirement in the task of improving the safety and maintainability of the structures. Among the multiple techniques available for health monitoring, acousto-ultrasonics (AU) offers the possibility of inspecting large areas of structures from a piezoelectric active sensor network with a relatively small number of sensors. This paper proposes the use of an active piezoelectric sensor network which uses PZTs transducers working as sensors or actuators in different actuation phases to inspect a wind turbine structure to determine the presence of damages by analysing the data from the sensors with multivariate statistical methods such as PCA and damages indices in order to discern damages from the healthy structure.