The term Carnot Battery refers to thermo-mechanical energy storage technologies that store electricity in the form of thermal exergy with electricity as the main output. The potential role of such ...technologies in future energy systems with a high renewable penetration has been increasingly acknowledged in recent years. This article provides a comprehensive and detailed review of the key components relevant to Carnot Batteries, which is highly relevant as the system performance hinges on the component characteristics. Focus is placed on compressors, expanders, thermal energy storage, heat exchangers and working fluids that have been and potentially will be applied to Carnot Batteries, covering their development status, technical performance, characteristic operating parameters, and cost functions. Based on the review and analyses, the most critical research barriers and development needs are highlighted for further development of the Carnot battery systems. This review represents the first of its kind, incorporating an extensive collection of key data for system modelling and optimization, technical performance evaluation, component selection and economic assessment for Carnot Batteries. These aspects are needed to bridge the gap between research and industrial applications, and can guide future research and development of key Carnot Battery components.
•Comprehensive technology review of key Carnot Battery components.•State-of-the-art review, performance and cost models provided for each component.•Component technical barriers and selection criteria for Carnot Batteries.•Results facilitate Carnot Battery modelling, design and techno-economic assessment.
This book examines the evolution of machine design methodology from the Renaissance to the Age of Machines in the 19th century. This premise is based in part on the work of da Vinci scholar Ladislo ...Reti who translated the last discovered work of Leonardo da Vinci in 1967. In the Codex Madrid, Reti found evidence that Leonardo planned to write a book on basic machine elements and compared the great artist-engineer's drawings to the work of 19th C. machine theorist Franz Reuleaux of Berlin. Reuleaux is credited with classifying the basic elements of machine design and also enumerating six basic classes of mechanisms to change motion from one form to another. Moon's book carries Reti's thesis further and provides detailed analysis, comparing design concepts of engineers of the 15th century Renaissance and the 19th century age of machines from a workshop tradition to the rational scientific discipline used today. The design ideas of Leonardo and Reuleaux are placed in the historical, economic and social context of their times. There is also an appendix with a short description of the famous 'theatre of machines' books of the 15th to the 18th centuries. This book makes use of the unique collection of 230 kinematic models of Reuleaux at Cornell University. Detailed comparisons of 20 basic machine mechanisms such as the slider crank and four-bar linkages in both Leonardo's drawings and Reuleaux's models are made. These models illustrate the elegance and aesthetics of machine design in the 19th century pioneered by Franz Reuleaux. The book hopes to convince the reader that the development of a rational design methodology for machines that grew from the time of Leonardo to the early 20th century was as great a feat as the invention of the machines themselves.
•Acoustic imaging is used to perform the diagnosis of a rotating machine.•Beamforming method allows obtaining the acoustic signals radiated by the machine.•The identified signals feed a spectral ...kurtosis (SK) algorithm.•Mapping the SK allows localizing impulsive sources in spatial and frequency domains.•The impulsive sources can eventually be related to faulted parts of the mechanism.
Rotating machines diagnosis is conventionally related to vibration analysis. Sensors are usually placed on the machine to gather information about its components. The recorded signals are then processed through a fault detection algorithm allowing the identification of the failing part. This paper proposes an acoustic-based diagnosis method. A microphone array is used to record the acoustic field radiated by the machine. The main advantage over vibration-based diagnosis is that the contact between the sensors and the machine is no longer required. Moreover, the application of acoustic imaging makes possible the identification of the sources of acoustic radiation on the machine surface. The display of information is then spatially continuous while the accelerometers only give it discrete. Beamforming provides the time-varying signals radiated by the machine as a function of space. Any fault detection tool can be applied to the beamforming output. Spectral kurtosis, which highlights the impulsiveness of a signal as function of frequency, is used in this study. The combination of spectral kurtosis with acoustic imaging makes possible the mapping of the impulsiveness as a function of space and frequency. The efficiency of this approach lays on the source separation in the spatial and frequency domains. These mappings make possible the localization of such impulsive sources. The faulty components of the machine have an impulsive behavior and thus will be highlighted on the mappings. The study presents experimental validations of the method on rotating machines.
Abstract
In order to make each functional module of silage machinery have a high degree of adaptability and meet the market demand of coordinated operation of each functional module, this paper ...preliminarily explores the symbiosis concept in the field of silage equipment, and applies the technology system evolution theory to the symbiotic design of silage equipment. In the design stage, the designers divide the functional modules of the silage machinery according to the market and user needs, and then analyze the symbiosis of the interrelated modules and screen out the functional modules with weak adaptability, so as to carry out the technical system evolution and optimize each functional module, and then establish the layout scheme between modules, so that the various functional modules of the silage machinery have strong adaptability, improve product quality, reduce design costs, shorten the design cycle, and realize the user’s demand for the coordinated operation of multi-functional silage machinery.
Abstract
Compared with the traditional mechanical engineering design, the intelligent mechanical engineering automation design has obvious economic applicability. In recent years, with the continuous ...development of China’s mechanical engineering design field, mechanical engineering design has been effectively innovated and promoted, automation technology as a representative of which also has an obvious role. In this paper, the concept of mechanical design, manufacturing and construction in the new era is expounded, and the principle and application of intelligent technology are analyzed for readers’ reference.
•Different kinds of fault-induced vibration signal modulation effects are formulated, illustrated and summarized.•An up-to-date methodological review for signal modulation feature extraction methods ...is provided.•Representative works of vibration signal modulation feature extraction-based fault diagnosis are reviewed and illustrated.
Rotating machinery faults can induce characteristic modulation effects in a vibration signal, and their diagnosis can thus be conducted by extracting fault-induced modulation features. To be specific, such a diagnostic strategy typically involves three main aspects, i.e., fault-induced signal modulation mechanisms, modulation feature extraction methods and applications for diagnosis. To date, the research works on these three aspects all achieved great progresses. A systematic review is thus urgently needed to summarize these achievements, and guide their future directions and developments. This paper aims to fill these gaps and address the needs. Three kinds of typical modulation effects induced by transmission elements are firstly reviewed and summarized. For each kind of modulation effects, a representative phenomenological model is given to formulate and illustrate the relationship between fault-induced modulation patterns and characteristic spectral distributions. As the primary tools to extract signal modulation features, different kinds of time-frequency analysis and signal decomposition methods are then systematically reviewed, including their up-to-date developments, classifications and application scenarios. Some representative works of vibration signal modulation feature extraction-based fault diagnosis are finally introduced along with several typical examples. Based on current research achievements, two potential topics for the future developments of vibration-based diagnostic techniques are the quantitative study of fault feature evolution patterns and the development of more robust and efficient feature extraction algorithms.
•ResNet-50 is improved to learn low-level features automatically.•Multi-scale feature extractor is embedded in models to decrease information loss.•Distance between domains is measured by conditional ...maximum mean discrepancy.•Case studies confirm the superiority and robustness of the proposed method.
The tremendous success of deep learning in machine fault diagnosis is dependent on the hypothesis that training and test datasets are subordinated to the same distribution. This subordination is difficult to meet in practical scenarios of industrial applications. On the one hand, the working conditions of rotating machinery can change easily. On the other hand, vibration data and labels are difficult to obtain to train a specific model for each working condition. In this study, we solve these problems by constructing a novel deep transfer learning model called multi-scale deep intra-class adaptation network, which first uses the modified ResNet-50 to extract low-level features and then constructs a multiple scale feature learner to analyze these low-level features at multiple scales and obtain high-level features as input for the classifier. Pseudo labels are then computed to shorten the conditional distribution distance of vibration data collected under different working loads for intra-class adaptation. The proposed method is validated using two datasets to recognize the bearing normal state, the inner race, the ball and outer race faults, and their fault degrees under four different working loads. The high-precision diagnosis results of 24 transfer learning experiments reveal the reliability and generalizability of the constructed model.
•A new system identification approach for the rotating machinery is proposed.•The corresponding modeling framework for the proposed approach is developed.•The method of building the frequency-domain ...version modeling dictionary is given.•The effect of the data sequence on the identification result is discussed theoretically.
In this study, the dynamic modeling of rotating machinery, which is a harmonic excitation system, is investigated based on a nonlinear autoregressive (NARX) model with external inputs. Generally, NARX model-based techniques require Gaussian (white) noise, and thus these methods are not suitable for rotating machinery. Although there have been some reports on the modeling of harmonic excitation systems, the existing methods cannot establish a single-input single-output (SISO) NARX model to represent the rotating machinery over a wide range of rotational speeds. An improved modeling method called the frequency sweep system identification approach is proposed in this study to solve this issue. A discrete-time Fourier transform (DTFT) is performed on the system input and output datasets over a wide speed range to obtain the resulting spectra, and the amplitudes corresponding to the rotational frequencies are extracted and spliced together to convert multiple time-domain signals into one input data set and one output data set composed of frequency-domain data. Then, the modeling process can be carried out using the orthogonal forward search algorithm. Moreover, the effect of the data sequence on the identification results is discussed theoretically. A key feature of the proposed method is that the model structure detection and coefficient calculation are conducted with spliced frequency-domain vectors. The feasibility of the proposed modeling approach is validated through numerical and experimental cases. This work is a supplement to existing modeling methods based on the NARX model and provides a modeling basis for the analysis and design of rotating machinery in combination with the NARX model.
•Conditions transfer and artificial-to-natural transfer are revealed and realized.•A unified framework with 7 methods is constructed on three datasets.•Perform Comparison analysis to provide a ...baseline for intelligent diagnosis.•Data dependency, transferability, and task relativity analysis are researched.
Rotating machinery intelligent diagnosis with large data has been researched comprehensively, while there is still a gap between the existing diagnostic model and the practical application, due to the variability of working conditions and the scarcity of fault samples. To address this problem, few-shot transfer learning method is constructed utilizing meta-learning for few-shot samples diagnosis in variable conditions in this paper. We consider two transfer situations of rotating machinery intelligent diagnosis named conditions transfer and artificial-to-natural transfer, and construct seven few-shot transfer learning methods based on a unified 1D convolution network for few-shot diagnosis of three datasets. Baseline accuracy under different sample capacity and transfer situations are provided for comprehensive comparison and guidelines. What is more, data dependency, transferability, and task plasticity of various methods in the few-shot scenario are discussed in detail, the data analysis result shows meta-learning holds the advantage for machine fault diagnosis with extremely few-shot instances on the relatively simple transfer task. Our code is available at https://github.com/a1018680161/Few-shot-Transfer-Learning.
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