Abstract:The growing complexity and interdependence of water management processes requires the involvement of multiple stakeholders in water governance. Multi-party collaboration is increasingly vital at both the strategy development and implementation levels. Multi-party collaboration involves a process of joint decision-making among key stakeholders in a problem domain directed towards the future of that domain. However, the common goal is not present from the beginning; rather, the common goal emerges during the process of collaboration. Unfortunately, when the conflicting interests of different actors are at stake, the large majority of environmental multi-party efforts often do not reliably deliver sustainable improvements to policy and/or practice. One of the reasons for this, which has been long established by many case studies, is that social learning with a focus on relational practices is missing. The purpose of this paper is to present the design and initial results of a pilot study that utilized a game-based approach to explore the effects of relational practices on the effectiveness of water governance. This paper verifies the methods used by addressing the following question: are game mechanisms, protocols for facilitation and observation, the recording of decisions and results, and participant surveys adequate to reliably test hypotheses about behavioral decisions related to water governance? We used the "Lords of the Valley" (LOV) game, which focuses on the local-level management of a hypothetical river valley involving many stakeholders. We used an observation protocol to collect data on the quality of relational practices and compared this data with the quantitative outcomes achieved by participants in the game. In this pilot study, we ran the game three times with different groups of participants, and here we provide the outcomes within the context of verifying and improving the methods.
Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.
Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.
The study presents the evaluation and comparative analysis of engine shaft line performance in maritime transport ships of the same type. During its operation, a technical system performs functions for which it was designed. It goes through different states. Dynamic state changes of a rotational system can be identified by means of its vibration measurement. For this purpose, a research was carried out which involved recording vibrations of the analysed rotational systems. The recordings were used for calculating selected characteristics in the time-domain, where one of the most unique is the value of the normalized mutual correlation function. On the basis of the concentration values, the characteristics which unambiguously determine the ability state were selected for further studies. Then an identification method for rotational system non-coaxiality was proposed. The method involves using fuzzy clustering. According to this method the values of input signal characteristics were used to formulate fuzzy clusters of system ability and inability states. The method can be used for identifying the current state of the system. The study presents the results of the application of this method in engine turbine shaft lines of minesweepers, with the rotational system selected as an example. It needs to be noted that the efficiency of identifying the operating state of the system with this method is higher than with other methods described in the literature by authors who deal with this issue. The research results have a significant impact on the evaluation of mechanical properties of the studied objects and directly affect operational states of mechanical systems, including those installed in minesweepers, thus determining their reliability.
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