Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Condition monitoring is essential to operate industrial assets safely and efficiently. To achieve this goal, the development of robust health indicators has recently attracted significant attention. These indicators, which provide quantitative real-time insights into the health status of industrial assets over time, serve as valuable tools for fault detection and prognostics. In this study, we propose a novel and universal approach to learn health indicators based on unsupervised contrastive learning. Operational time acts as a proxy for the asset's degradation state, enabling the learning of a contrastive feature space that facilitates the construction of a health indicator by measuring the distance to the healthy condition. To highlight the universality of the proposed approach, we assess the proposed contrastive learning framework in two distinct tasks - wear assessment and fault detection - across two different case studies: a milling machines case study and a real condition monitoring case study of railway wheels from operating trains. First, we evaluate if the health indicator is able to learn the real health condition on a milling machine case study where the ground truth wear condition is continuously measured. Second, we apply the proposed method on a real case study of railway wheels where the ground truth health condition is not known. Here, we evaluate the suitability of the learned health indicator for fault detection of railway wheel defects. Our results demonstrate that the proposed approach is able to learn the ground truth health evolution of milling machines and the learned health indicator is suited for fault detection of railway wheels operated under various operating conditions by outperforming state-of-the-art methods. Further, we demonstrate that our proposed approach is universally applicable to different systems and different health conditions

Advances in Sensors and Sensing for Technical Condition Assessment and NDT

The adequate assessment of key apparatus conditions is a hot topic in all branches of industry. Various online and offline diagnostic methods are widely applied to provide early detections of any abnormality in exploitation. Furthermore, different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential fault. The essential steps of the signal analysis regarding the technical condition assessment process may be listed as: signal measurement (using relevant sensors), processing, modelling, and classification. In the Special Issue entitled “Advances in Sensors and Sensing for Technical Condition Assessment and NDT”, we present the latest research in various areas of technology

Adaptation of domestic state governance to international governance models

The purpose of the article is to provide the evolving international trends of modern management models and authorial vision of model of state governance system in Ukraine, its subsystems, in particular, the system of provision of administrative services that is appropriate for the contemporary times. Methodology. On the basis of scientific and theoretical approaches to the definitions of terms “state governance” and “public governance”, there was an explanation of considerable difference between them and, taking into consideration, the mentality of Ukrainian society and peculiar weak side in self-organization, the authors offered to form authorial model of governance on the basis of historically traditional for Ukraine model of state governance and to add some elements of management concepts that proved their significance, efficiency and priority in practice. Results. The authors emphasized the following two prevailing modern management models in the international practice: “new state management” and “good governance”. The first concept offered for consideration served as a basis for the semantic content of state activity that reflects more the state of administrative reformation. Practical meaning. A practical introduction of management to the domestic model of governance creates the range of contradictions that do not allow implementing herein concept. Pursuant to authors, the second one allows in considerable measure to reform state governance, considering historically developed peculiarities of this model. Moreover, the involvement of concept herein into introduction of informational and communicational technologies in the process of governance eliminates the necessity of power decentralization, it allows to form real net structure and, at the same, to keep vertical power structure, to involve citizens for formation and taking of management decisions, to form electronic communicational channel of feedback, to provide citizens with electronic administrative services. All indicated advantages of the concept certify about the necessity to reform state governance exactly in this field. Meaning/ Distinction. This article raises a question about the significance of formation and sequence of state policy in Ukraine aimed at creating an information-oriented society, space, as well as informational and technological infrastructure

Non-contact vision-based deformation monitoring on bridge structures

Information on deformation is an important metric for bridge condition and performance assessment, e.g. identifying abnormal events, calibrating bridge models and estimating load carrying capacities, etc. However, accurate measurement of bridge deformation, especially for long-span bridges remains as a challenging task. The major aim of this research is to develop practical and cost-effective techniques for accurate deformation monitoring on bridge structures. Vision-based systems are taken as the study focus due to a few reasons: low cost, easy installation, desired sample rates, remote and distributed sensing, etc. This research proposes an custom-developed vision-based system for bridge deformation monitoring. The system supports either consumer-grade or professional cameras and incorporates four advanced video tracking methods to adapt to different test situations. The sensing accuracy is firstly quantified in laboratory conditions. The working performance in field testing is evaluated on one short-span and one long-span bridge examples considering several influential factors i.e. long-range sensing, low-contrast target patterns, pattern changes and lighting changes. Through case studies, some suggestions about tracking method selection are summarised for field testing. Possible limitations of vision-based systems are illustrated as well. To overcome observed limitations of vision-based systems, this research further proposes a mixed system combining cameras with accelerometers for accurate deformation measurement. To integrate displacement with acceleration data autonomously, a novel data fusion method based on Kalman filter and maximum likelihood estimation is proposed. Through field test validation, the method is effective for improving displacement accuracy and widening frequency bandwidth. The mixed system based on data fusion is implemented on field testing of a railway bridge considering undesired test conditions (e.g. low-contrast target patterns and camera shake). Analysis results indicate that the system offers higher accuracy than using a camera alone and is viable for bridge influence line estimation. With considerable accuracy and resolution in time and frequency domains, the potential of vision-based measurement for vibration monitoring is investigated. The proposed vision-based system is applied on a cable-stayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. Analysis results indicate that the measured data enables accurate estimation of modal frequencies and could be used to investigate variations of modal frequencies under varying pedestrian loads. The vision-based system in this application is used for multi-point vibration measurement and provides results comparable to those obtained using an array of accelerometers

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

Net structure of subject-to-subject relations in the management of the system of administrative services provision

The purpose of the work is to form the net structure of management of the system of administrative services provision on the basis of implementation of subject-to-subject interactions between state sector and civil society. Methodology. The methodology basis for the investigation is the abstract-logical analysis of theoretical and methodological backgrounds for management of relations and interactions. For the theoretical generalization and formation of net structure, there are used scientific recommendations of Ukrainian scientists regarding the necessity to implement subject-to-subject relations in the system of administrative services provision. Results. The investigations allowed confirming that the hierarchical structure of the state governance system does not give an opportunity to implement equal interaction between a subject of provision and a subject of an appeal as these relations have one – way communication and the feedback channel has a formal character. Moreover, the civil society is not considered by state sector to be a source of methods and ways to develop the system of state governance, in particular, the management system of administrative services provision. Practical meaning. The net structure of management will allow implementing the subject-subject relations in the system, under which the actions of the subject of provision – that means state sector – will be directed to the realization of rights and interests of the subjects of appeal. In their turn, apart from the performance of all legislative responsibilities that they should perform, they can carry out activities directed to the development of management activity in the system of administrative services provision and the whole system of state governance as an integral system of management. Meaning/Distinction. The provided model of the net structure will allow involving citizens in the processes of state governance and increasing the impact of the civil sector during the making of state and management decisions and, as a result, to confirm subject-to-subject positions in the relations

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Local regularization assisted split augmented Lagrangian shrinkage algorithm for feature selection in condition monitoring

\ua9 2024 The Author(s)Feature selection plays a vital role in improving the efficiency and accuracy of condition monitoring by constructing sparse but effective models. In this study, an advanced feature selection algorithm named the local regularization assisted split augmented Lagrangian shrinkage algorithm (LR-SALSA) is proposed. The feature selection is realized by solving a l1-norm optimization problem, which usually selects more sparse and representative features at less computational costs. The proposed algorithm operates in two stages, namely variable selection and coefficient estimation. In the stage of variable selection, the primal problem is converted into three subproblems which can be solved separately. Then individual penalty parameters are applied to every coefficient of the model when dealing with the first subproblem. Under the Bayesian evidence framework, an iterative algorithm is derived to optimize these hyperparameters. During the optimization process, redundant variables will be pruned to guarantee model sparsity and improve computational efficiency at the same time. In the second stage, the coefficients for the selected model terms are determined using the least squares technique. The superior performance and efficiency of the proposed LR-SALSA method are validated through two numerical examples and a real-world cutting tool wear prediction case study. Compared with the existing methods, the proposed method can generate a sparse model and ensure a good trade-off between estimation accuracy and computational efficiency