This article reports our study on a reduced adaptive fuzzy decoupling control for our lower limb exoskeleton system which typically is a multi-input–multi-output (MIMO) uncertain nonlinear system. To show the applicability and generality of the proposed control methods, a more general MIMO uncertain nonlinear system model is considered. By decoupling control, the entire MIMO system is separated into several MISO subsystems. In our experiments, such a system may have problems (even unstable) if a traditional fuzzy approximator is used to estimate the complicated coupling terms. In this article, to overcome this problem, a reduced adaptive fuzzy system together with a compensation term is proposed. Compared to traditional approaches, the proposed fuzzy control approach can reduce possible chattering phenomena and achieve better control performance. By employing the proposed control scheme to an actual 2-DOF lower limb exoskeleton rehabilitation robot system, it can be seen from the experimental results that, as expected, it has good performance to track the model trajectory of a human walking gait. Therefore, it can be concluded that the developed approach is effective for the control of a lower limb exoskeleton system.

Reduced Adaptive Fuzzy Decoupling Control for Lower Limb Exoskeleton

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications.

Structural health monitoring (SHM) is a very important area in a wide spectrum of fields and engineering applications. With an SHM system, it is possible to reduce the number of non-necessary inspection tasks, the associated risk and the maintenance cost in a wide range of structures during their lifetime. One of the problems in the detection and classification of damage are the constant changes in the operational and environmental conditions. Small changes of these conditions can be considered by the SHM system as damage even though the structure is healthy. Several applications for monitoring of structures have been developed and reported in the literature, and some of them include temperature compensation techniques. In real applications, however, digital processing technologies have proven their value by: (i) offering a very interesting way to acquire information from the structures under test; (ii) applying methodologies to provide a robust analysis; and (iii) performing a damage identification with a practical useful accuracy. This work shows the implementation of an SHM system based on the use of piezoelectric (PZT) sensors for inspecting a structure subjected to temperature changes. The methodology includes the use of multivariate analysis, sensor data fusion and machine learning approaches. The methodology is tested and evaluated with aluminum and composite structures that are subjected to temperature variations. Results show that damage can be detected and classified in all of the cases in spite of the temperature changes.

https://www.mdpi.com/1424-8220/17/6/1252/pdf

Distributed Piezoelectric Sensor System for Damage Identification in Structures Subjected to Temperature Changes.

Recent developments in artificial intelligence (AI), in particular machine learning (ML), have been significantly advancing smart city applications. Smart infrastructure, which is an essential component of smart cities, is equipped with wireless sensor networks that autonomously collect, analyze, and communicate structural data, referred to as “smart monitoring”. AI algorithms provide abilities to process large amounts of data and to detect patterns and features that would remain undetected using traditional approaches. Despite these capabilities, the application of AI algorithms to smart monitoring is still limited due to mistrust expressed by engineers towards the generally opaque AI inner processes. To enhance confidence in AI, the “black-box” nature of AI algorithms for smart monitoring needs to be explained to the engineers, resulting in so-called “explainable artificial intelligence” (XAI). However, when aiming at improving the explainability of AI algorithms through XAI for smart monitoring, the variety of AI algorithms requires proper categorization. Therefore, this review paper first identifies objectives of smart monitoring, serving as a basis to categorize AI algorithms or, more precisely, ML algorithms for smart monitoring. ML algorithms for smart monitoring are then reviewed and categorized. As a result, an overview of ML algorithms used for smart monitoring is presented, providing an overview of categories of ML algorithms for smart monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.

Artificial Intelligence Techniques for Smart City Applications

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

https://www.mdpi.com/1424-8220/20/17/4834/pdf

Nonlinear feature extraction through manifold learning in an electronic tongue classification task

Structural Health Monitoring is a growing area of interest given the benefits obtained from its use This area includes different tasks in the damage identification process, among them, the most important is the damage detection at an early stage which enables to increase the security in mechanisms and systems, reducing risks and avoiding accidents As a contribution in this topic, this work presents a data-driven methodology for the detection and classification of damages by using multivariate data driven approaches and machine learning algorithms which are validated and compared by using data from real structures in order to determine its behavior In the methodology, PCA (Principal component analysis) and some pre-processing steps are used as the mechanisms to reduce data and build the features vector with relevant information about the different states of the structures under test This methodology is validated by using some aluminum plates which are instrumented and inspected by means of PZT transducers attached to them and working in in several actuation phases Results show a properly damage detection and classification of different simulated and real-damages

Structural damage detection and classification based on machine learning algorithms

Structural health monitoring (SHM) is an important research area, which interest is the damage identification process. Different information about the state of the structure can be obtained in the process, among them, detection, localization and classification of damages are mainly studied in order to avoid unnecessary maintenance procedures in civilian and military structures in several applications. To carry out SHM in practice, two different approaches are used, the first is based on modelling which requires to build a very detailed model of the structure, while the second is by means of data-driven approaches which use information collected from the structure under different structural states and perform an analysis by means of data analysis . For the latter, statistical analysis and pattern recognition have demonstrated its effectiveness in the damage identification process because real information is obtained from the structure through sensors installed permanently to the observed object allowing a real-time monitoring. This chapter describes a damage detection and classification methodology, which makes use of a piezoelectric active system which works in several actuation phases and that is attached to the structure under evaluation, principal component analysis, and machine learning algorithms working as a pattern recognition methodology. In the chapter, the description of the developed approach and the results when it is tested in one aluminum plate are also included.

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Data-driven methodologies for structural damage detection based on machine learning applications

Structural Damage Assessment using an Artificial Immune System

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.

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

One of the goals of structural health monitoring (SHM) applications is to determine the presence and the severity of a damage. In some cases, this is an element to forecast the behaviour and take decisions to allocate maintenance or replace the structure or the piece. An appropriate decision can reduce the risk of an accident, making more efficient the management of maintenance tasks and reducing the costs while improving the performance of a system. In this way, the development of a good SHM system is a need. Through the use of: (i) advanced methodologies of digital signal processing; (ii) the acquisition of information from a set of piezoelectric sensors appropriately placed in the surface of a structure; and (iii) the use of techniques such as principal component analysis (PCA) [1, 2, 3] and
machine learning, it is possible to generate a solution that meets the necessity about the knowledge of
the structural state. This work presents a methodology which allow to determine the presence of a structural damage and its classification in spite of temperature changes. The methodology is tested with a composite plate instrumented with a PZT sensor network and some added masses as damages. The whole system is validated to different temperatures.

Maribel Anaya Vejar

Papers

Structural damage detection and classification based on machine learning algorithms

Data-driven methodologies for structural damage detection based on machine learning applications

Structural Damage Assessment using an Artificial Immune System

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

A machine learning methodology for structural damage classification in structural health monitoring