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Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound --- n/a

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Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

Technology: general issues --- History of engineering & technology --- artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound --- n/a

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Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis.

Technology: general issues --- rolling bearing --- performance degradation --- hybrid kernel function --- krill herd algorithm --- SVR --- acoustic-based diagnosis --- gear fault diagnosis --- attention mechanism --- convolutional neural network --- stacked auto-encoder --- weighting strategy --- deep learning --- bearing fault diagnosis --- intelligent leak detection --- acoustic emission signals --- statistical parameters --- support vector machine --- wavelet denoising --- Shannon entropy --- adaptive noise reducer --- gaussian reference signal --- gearbox fault diagnosis --- one against on multiclass support vector machine --- varying rotational speed --- fault detection and diagnosis --- faults estimation --- actuator and sensor fault --- observer design --- Takagi-Sugeno fuzzy systems --- automotive --- perception sensor --- lidar --- fault detection --- fault isolation --- fault identification --- fault recovery --- fault diagnosis --- fault detection and isolation (FDIR) --- autonomous vehicle --- model predictive control --- path tracking control --- fault detection and isolation --- braking control --- nonlinear systems --- fault tolerant control --- iterative learning control --- neural networks --- cryptography --- wireless sensor networks --- machine learning --- scan-chain diagnosis --- artificial neural network --- NARX --- control valve --- decision tree --- signature matrix --- n/a

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Multiscale entropy (MSE) measures to evaluate the complexity of time series by taking into account the multiple time scales in physical systems were proposed in the early 2000s. Since then, these approaches have received a great deal of attention and have been used in a wide range of applications. Multivariate approaches have also been developed. The algorithms for an MSE approach are composed of two main steps: (i) a coarse-graining procedure to represent the system’s dynamics on different scales and (ii) the entropy computation for the original signal and for the coarse-grained time series to evaluate the irregularity for each scale. Moreover, different entropy measures have been associated with the coarse-graining approach, each one having its advantages and drawbacks. In this Special Issue, we gathered 24 papers focusing on either the theory or applications of MSE approaches. These papers can be divided into two groups: papers that propose new developments in entropy-based measures or improve the understanding of existing ones (9 papers) and papers that propose new applications of existing entropy-based measures (14 papers). Moreover, one paper presents a review of cross-entropy methods and their multiscale approaches.

History of engineering & technology --- electrocardiogram --- heart rate variability --- multiscale distribution entropy --- RR interval --- short-term inter-beat interval --- Alzheimer disease --- functional near infra-red spectroscopy --- signal complexity --- clock drawing test --- digit span test --- corsi block tapping test --- structural health monitoring --- multi-scale --- composite cross-sample entropy --- PD --- fault diagnosis --- variational mode decomposition --- multi-scale dispersion entropy --- HMSVM --- multiscale entropy --- embodied media --- tele-communication --- humanoid --- prefrontal cortex --- human behavior --- complexity --- page view --- sample entropy --- Wikipedia --- missing values --- physiological data --- medical information --- postural stability index --- stability states --- ensemble empirical mode decomposition --- gait --- Multiscale Permutation Entropy --- ordinal patterns --- estimator variance --- Cramér–Rao Lower Bound --- finite-length signals --- nonlinear dynamics --- multiscale indices --- cardiac risk stratification --- Holter --- long term monitoring --- multifractal spectrum --- multiscale time irreversibility --- predictability --- multiscale analysis --- entropy rate --- memory effect --- financial time series --- entropy --- cardiac autonomic neuropathy --- diabetes --- mental workload --- motif --- multi-scale entropy --- permutation entropy --- HRV --- SVM --- multivariate multiscale dispersion entropy --- multivariate time series --- electroencephalogram --- magnetoencephalogram --- CPD --- EEG --- sleep staging --- tensor decomposition --- preterm neonate --- bearing fault diagnosis --- weak fault --- multi-component signal --- local robust principal component analysis --- multi-scale permutation entropy --- brain complexity --- dynamic functional connectivity --- edge complexity --- fluid intelligence --- node complexity --- resting-state functional magnetic resonance imaging --- aging --- consolidation --- default mode network --- episodic memory --- fMRI --- network complexity --- resting state --- copula density --- dependency structures --- Voronoi decomposition --- ambient temperature --- telemetry --- systolic blood pressure --- pulse interval --- thermoregulation --- vasopressin --- center of pressure --- falls --- postural control --- cross-entropy --- multiscale cross-entropy --- asynchrony --- coupling --- cross-sample entropy --- cross-approximate entropy --- cross-distribution entropy --- cross-fuzzy entropy --- cross-conditional entropy --- eye movement events detection --- nonlinear analysis time series analysis --- approximate entropy --- fuzzy entropy --- multilevel entropy map --- time-scale decomposition --- heart sound --- ICEEMDAN --- RCMDE --- Fisher ratio --- biometric characterization --- multi-scale entropy (MSE) --- vector autoregressive fractionally integrated (VARFI) models --- heart rate variability (HRV) --- systolic arterial pressure (SAP) --- multivariate data

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Multiscale entropy (MSE) measures to evaluate the complexity of time series by taking into account the multiple time scales in physical systems were proposed in the early 2000s. Since then, these approaches have received a great deal of attention and have been used in a wide range of applications. Multivariate approaches have also been developed. The algorithms for an MSE approach are composed of two main steps: (i) a coarse-graining procedure to represent the system’s dynamics on different scales and (ii) the entropy computation for the original signal and for the coarse-grained time series to evaluate the irregularity for each scale. Moreover, different entropy measures have been associated with the coarse-graining approach, each one having its advantages and drawbacks. In this Special Issue, we gathered 24 papers focusing on either the theory or applications of MSE approaches. These papers can be divided into two groups: papers that propose new developments in entropy-based measures or improve the understanding of existing ones (9 papers) and papers that propose new applications of existing entropy-based measures (14 papers). Moreover, one paper presents a review of cross-entropy methods and their multiscale approaches.

electrocardiogram --- heart rate variability --- multiscale distribution entropy --- RR interval --- short-term inter-beat interval --- Alzheimer disease --- functional near infra-red spectroscopy --- signal complexity --- clock drawing test --- digit span test --- corsi block tapping test --- structural health monitoring --- multi-scale --- composite cross-sample entropy --- PD --- fault diagnosis --- variational mode decomposition --- multi-scale dispersion entropy --- HMSVM --- multiscale entropy --- embodied media --- tele-communication --- humanoid --- prefrontal cortex --- human behavior --- complexity --- page view --- sample entropy --- Wikipedia --- missing values --- physiological data --- medical information --- postural stability index --- stability states --- ensemble empirical mode decomposition --- gait --- Multiscale Permutation Entropy --- ordinal patterns --- estimator variance --- Cramér–Rao Lower Bound --- finite-length signals --- nonlinear dynamics --- multiscale indices --- cardiac risk stratification --- Holter --- long term monitoring --- multifractal spectrum --- multiscale time irreversibility --- predictability --- multiscale analysis --- entropy rate --- memory effect --- financial time series --- entropy --- cardiac autonomic neuropathy --- diabetes --- mental workload --- motif --- multi-scale entropy --- permutation entropy --- HRV --- SVM --- multivariate multiscale dispersion entropy --- multivariate time series --- electroencephalogram --- magnetoencephalogram --- CPD --- EEG --- sleep staging --- tensor decomposition --- preterm neonate --- bearing fault diagnosis --- weak fault --- multi-component signal --- local robust principal component analysis --- multi-scale permutation entropy --- brain complexity --- dynamic functional connectivity --- edge complexity --- fluid intelligence --- node complexity --- resting-state functional magnetic resonance imaging --- aging --- consolidation --- default mode network --- episodic memory --- fMRI --- network complexity --- resting state --- copula density --- dependency structures --- Voronoi decomposition --- ambient temperature --- telemetry --- systolic blood pressure --- pulse interval --- thermoregulation --- vasopressin --- center of pressure --- falls --- postural control --- cross-entropy --- multiscale cross-entropy --- asynchrony --- coupling --- cross-sample entropy --- cross-approximate entropy --- cross-distribution entropy --- cross-fuzzy entropy --- cross-conditional entropy --- eye movement events detection --- nonlinear analysis time series analysis --- approximate entropy --- fuzzy entropy --- multilevel entropy map --- time-scale decomposition --- heart sound --- ICEEMDAN --- RCMDE --- Fisher ratio --- biometric characterization --- multi-scale entropy (MSE) --- vector autoregressive fractionally integrated (VARFI) models --- heart rate variability (HRV) --- systolic arterial pressure (SAP) --- multivariate data