•Surveys on recent applications of artificial intelligence techniques to rotating machinery fault diagnosis.•Provides a guidance of how to choose and use artificial intelligence techniques in ...engineering.•Describes the artificial intelligence techniques applications and rotating machinery fault diagnosis trends.
Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.
•Main PHM challenges in industry 4.0: physics, data and solution requirements.•Data challenges: missing of anomalies, labels and the continuously monitored data.•Advancing methods of detection, ...diagnostics and prognostics for data challenge.•Solution requirements challenge: interpretability, security, uncertainty of models.
We are performing the digital transition of industry, living the 4th industrial revolution, building a new World in which the digital, physical and human dimensions are interrelated in complex socio-cyber-physical systems. For the sustainability of these transformations, knowledge, information and data must be integrated within model-based and data-driven approaches of Prognostics and Health Management (PHM) for the assessment and prediction of structures, systems and components (SSCs) evolutions and process behaviors, so as to allow anticipating failures and avoiding accidents, thus, aiming at improved safe and reliable design, operation and maintenance.
There is already a plethora of methods available for many potential applications and more are being developed: yet, there are still a number of critical problems which impede full deployment of PHM and its benefits in practice. In this respect, this paper does not aim at providing a survey of existing works for an introduction to PHM nor at providing new tools or methods for its further development; rather, it aims at pointing out main challenges and directions of advancements, for full deployment of condition-based and predictive maintenance in practice.
Explores methods for the representation and treatment of uncertainty in risk assessment In providing guidance for practical decision-making situations concerning high-consequence technologies (e.g., ...nuclear, oil and gas, transport, etc.), the theories and methods studied in Uncertainty in Risk Assessment have wide- ranging applications from engineering and medicine to environmental impacts and natural disasters, security, and financial risk management. The main focus, however, is on engineering applications. While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, Uncertainty in Risk Assessment can be read profitably by a broad audience of professionals in the field, including researchers and graduate students on courses within risk analysis, statistics, engineering, and the physical sciences. Uncertainty in Risk Assessment: * Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts. * Provides simple explanations (supported by straightforward numerical examples) of the meaning of different types of probabilities, including interval probabilities, and the fundamentals of possibility theory and evidence theory. * Offers guidance on when to use probability and when to use an alternative representation of uncertainty. * Presents and discusses methods for the representation and characterization of uncertainty in risk assessment. * Uses examples to clearly illustrate ideas and concepts.
The local irregularity and global correlation of wind speed can be described by the fractal dimension
D
and the Hurst parameter
H
. However, the existing mathematical models imply a linear ...relationship between the fractal dimension
D
and the Hurst parameter
H
, which is not adequate to describe the complete characteristics of the wind speed. To overcome the descriptive limitations of the
D
-
H
linearity assumption, in this paper, we introduce novel model, based on the generalized Cauchy (GC) process, for simulation and forecasting of wind speed. In the model, the fractal dimension
D
and Hurst parameter
H
can be combined arbitrarily to describe the local irregularity and global correlation of the wind speed. Furthermore, the GC process is taken as the disturbance fluctuation term in the forecasting model, and a difference iteration form is obtained as difference equation and incremental distribution. In such model, the maximum forecasting range of the wind speed is determined by the maximum Lyapunov exponent. To illustrate the properties of the model and its performance, simulating and forecasting of the actual wind speeds in two regions are reported.
With the increasing penetration of electric vehicles (EVs), more and more interactions appear between the transportation system and the power system, which might provide new hazards and channels for ...the proliferation of failures across the boundaries of the individual systems. In this context, this paper proposes an integrated risk assessment framework for an electric power system, considering scenarios that involve the electrified transportation system enabled by EVs charging technology in New York (NY) State. Firstly, scenarios in the transportation network of NY State, e.g. of reduced capacity and incident, are generated by a Monte Carlo non-sequential algorithm. Then, the cell transmission model (CTM) is used to simulate the evolution of the traffic flows under such scenarios. This allows evaluating the spatial-temporal EV charging loads in different areas of the electrified transportation system of NY State. Correspondingly, the running parameters in the studied power system are updated by the alternative current (AC) power flow model. Finally, the risk for the power system coming from the transportation system scenarios is assessed within a probabilistic risk analysis framework. The proposed integrated risk assessment framework is able to model the propagation of the effects of scenarios in the transportation system onto the power system of NY State and quantify the consequences. A real test case is used to illustrate the proposed framework.
•Weather extreme conditions affect power grid failures/repairs and data is scarce.•A stochastic resilience framework is proposed for assess the weather-grid interaction.•A power-flow emulator is ...constructed to reduce the computational cost.•Imprecise probabilistic methodology is used to tackle lack of data issues.•Most relevant weather-grid factors are identified by the global sensitivity analysis.
A generalised uncertainty quantification framework for resilience assessment of weather-coupled, repairable power grids is presented. The framework can be used to efficiently quantify both epistemic and aleatory uncertainty affecting grid-related and weather-related factors. The power grid simulator has been specifically designed to model interactions between severe weather conditions and grid dynamic states and behaviours, such as weather-induced failures or delays in components replacements. A resilience index is computed by adopting a novel algorithm which exploits a vectorised emulator of the power-flow solver to reduce the computational efforts. The resilience stochastic modelling framework is embedded into a non-intrusive generalised stochastic framework, which enables the analyst to quantify the effect of parameters imprecision. A modified version of the IEEE 24 nodes reliability test system has been used as representative case study. The surrogate-based model and the Power-Flow-based model are compared, and the results show similar accuracy but enhanced efficiency of the former. Global sensitivity of the resilience index to increasing imprecision in parameters of the probabilistic model has been analysed. The relevance of specific weather/grid uncertain factors is highlighted by global sensitivity analysis and the importance of dealing with imprecision in the information clearly emerges.
The rapid development of big data and smart technology in the natural gas industry requires timely and accurate forecasting of natural gas consumption on different time horizons. In this work, we ...propose a robust hybrid hours-ahead gas consumption method by integrating Wavelet Transform, RNN-structured deep learning and Genetic Algorithm. The Wavelet Transform is used to reduce the complexity of the forecasting tasks by decomposing the original series of gas loads into several sub-components. The RNN-structured deep learning method is built up via combining a multi-layer Bi-LSTM model and a LSTM model. The multi-layer Bi-LSTM model can comprehensively capture the features in the sub-components and the LSTM model is used to forecast the future gas consumption based on these abstracted features. To enhance the performance of the RNN-structured deep learning model, Genetic Algorithm is employed to optimize the structure of each layer in the model. Besides, the dropout technology is applied in this work to overcome the potential problem of overfitting. In this case study, the effectiveness of the developed method is verified from multiple perspective, including graphical examination, mathematical errors analysis and model comparison, on different data sets.
•This work focuses on hourly natural gas demand forecasting.•A novel hybrid RNN-based demand forecasting method is developed.•The effectiveness of the forecasting method is tested.
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•A method is developed to analyze reliability of supply of gas pipeline networks.•Uncertainty, complexity and physical constraint are considered in the method.•Indices are developed ...with respect to global system and individual customer.•The application of the method is studied in a complex natural gas pipeline network.
A systematic method is developed for supply reliability assessment of natural gas pipeline networks. In the developed method, the integration of stochastic processes, graph theory and thermal-hydraulic simulation is performed accounting for uncertainty and complexity. The supply capacity of a pipeline network depends on the unit states and the network structure, both of which change stochastically because of stochastic failures of the units. To describe this, in this work a capacity network stochastic model is developed, based on Markov modeling and graph theory. The model is embedded in an optimization algorithm to compute the capacities of the pipeline network under different scenarios and analyze the consequences of failures of units in the system. Indices of supply reliability and risk are developed with respect to two aspects: global system and individual customers. In the case study, a gas pipeline network is considered and the results are analyzed in detail.
Short-term power load forecasting plays a key role in power supply systems. Many methods have been used in short-term power load forecasting during the past years. A new short-term power load ...forecasting method is proposed in this study. First, the study represents a Fractional Auto-regressive Integrated Moving Average (FARIMA) model based on long-range dependence (LRD). The LRD model is governed by the Hurst exponent, which shows whether the model exhibits the LRD or not. Then, the study employs Cuckoo Search (CS) algorithm based on two parameters dynamic adjustment for parameter optimization of the forecasting model. As test problem, we use the real power consumption data, and test it for different forecasting models. Our results indicate that the FARIMA model and the improved optimization algorithm show relatively high accuracy and effectiveness in forecasting short-term power load.
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