Application of EMD-AR and MTS for hydraulic pump fault diagnosis

A real-time diagnosis of hydraulic pumps is very crucial for the reliable operation of hydraulic systems. The main purpose of this study is to propose a fault diagnosis approach for hydraulic systems based on the empirical mode decomposition (EMD), autoregressive (AR) model, singular value decomposition (SVD), and Mahalanobis–Taguchi system (MTS). The AR model effectively extracts the fault feature of vibration signals. However, it can only be applied to stationary signals; the fault vibration signals of hydraulic pumps are non-stationary. To address this problem, the EMD method is used as a pretreatment step to decompose the non-stationary vibration signals of hydraulic pumps. First, the vibration signals of hydraulic pumps are decomposed into a finite number of stationary intrinsic mode functions (IMF). The AR model of each IMF component is established. The AR parameters and the remnant’s variance are regarded as the initial feature vector matrices. Third, the singular values are obtained by applying the SVD to the initial feature vector matrices. Finally, these values serve as the fault feature vectors to be entered to the MTS, thereby classifying the fault pattern of the hydraulic pumps. The Taguchi methods are employed to reduce the redundant features and extract the principal components. Experimental analysis results indicate that this method can effectively accomplish the fault diagnosis of hydraulic pumps

Fairness-aware Network Revenue Management with Demand Learning

In addition to maximizing the total revenue, decision-makers in lots of industries would like to guarantee fair consumption across different resources and avoid saturating certain resources. Motivated by these practical needs, this paper studies the price-based network revenue management problem with both demand learning and fairness concern about the consumption across different resources. We introduce the regularized revenue, i.e., the total revenue with a fairness regularization, as our objective to incorporate fairness into the revenue maximization goal. We propose a primal-dual-type online policy with the Upper-Confidence-Bound (UCB) demand learning method to maximize the regularized revenue. We adopt several innovative techniques to make our algorithm a unified and computationally efficient framework for the continuous price set and a wide class of fairness regularizers. Our algorithm achieves a worst-case regret of $\tilde O(N^{5/2}\sqrt{T})$, where $N$ denotes the number of products and $T$ denotes the number of time periods. Numerical experiments in a few NRM examples demonstrate the effectiveness of our algorithm for balancing revenue and fairness

Rolling Bearing Fault Diagnosis Based on EMD-TEO and Mahalanobis Distance

A intelligent rolling bearing fault diagnosis method is proposed on Empirical Mode Decomposition (EMD) – Teager Energy Operator (TEO) and Mahalanobis distance. EMD can adaptively decompose vibration signal into a series of Intrinsic Mode Functions (IMFs), that is, zero mean mono-component AM-FM signal. TEO can estimate the total mechanical energy required to generate signals, so it has good time resolution and self-adaptive ability to the transient of the signal, which shows the advantage to detect the signal impact characteristics. With regards to the impulse feature of the bearing fault vibration signals, TEO can be used to detect cyclical impulse characteristic caused by bearing failure, gain the instantaneous amplitude spectrum of each IMF component, then identify the characteristic frequency of the interesting and single IMF component in bearing faults by means of Teager energy spectrum. The amplitude of the Teager energy spectrum in inner race fault frequency, outer fault frequency and the ratio of the energy of the resonance frequency to the total energy were extracted as the feature vectors, which were used as training samples and test samples separately for fault diagnosis. Then the Mahalanobis distances between the real measure and different type overalls of fault sample are calculated to classify the real condition of rolling bearing. Experimental results was concluded that this method can accurately identify and diagnose different fault types of rolling bearing

Pre-Trained Model Recommendation for Downstream Fine-tuning

As a fundamental problem in transfer learning, model selection aims to rank off-the-shelf pre-trained models and select the most suitable one for the new target task. Existing model selection techniques are often constrained in their scope and tend to overlook the nuanced relationships between models and tasks. In this paper, we present a pragmatic framework \textbf{Fennec}, delving into a diverse, large-scale model repository while meticulously considering the intricate connections between tasks and models. The key insight is to map all models and historical tasks into a transfer-related subspace, where the distance between model vectors and task vectors represents the magnitude of transferability. A large vision model, as a proxy, infers a new task's representation in the transfer space, thereby circumventing the computational burden of extensive forward passes. We also investigate the impact of the inherent inductive bias of models on transfer results and propose a novel method called \textbf{archi2vec} to encode the intricate structures of models. The transfer score is computed through straightforward vector arithmetic with a time complexity of $\mathcal{O}(1)$. Finally, we make a substantial contribution to the field by releasing a comprehensive benchmark. We validate the effectiveness of our framework through rigorous testing on two benchmarks. The benchmark and the code will be publicly available in the near future

An Integrated Approach for Assessing Aquatic Ecological Carrying Capacity: A Case Study of Wujin District in the Tai Lake Basin, China

Aquatic ecological carrying capacity is an effective method for analyzing sustainable development in regional water management. In this paper, an integrated approach is employed for assessing the aquatic ecological carrying capacity of Wujin District in the Tai Lake Basin, China. An indicator system is established considering social and economic development as well as ecological resilience perspectives. While calculating the ecological index, the normalized difference vegetation index (NDVI) is extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series images, followed by spatial and temporal analysis of vegetation cover. Finally, multi-index assessment of aquatic ecological carrying capacity is carried out for the period 2000 to 2008, including both static and dynamic variables. The results reveal that aquatic ecological carrying capacity presents a slight upward trend in the past decade and the intensity of human activities still exceeded the aquatic ecological carrying capacity in 2008. In terms of human activities, population has decreased, GDP has quadrupled, and fertilizer application and industrial wastewater discharge have declined greatly in the past decade. The indicators representing aquatic ecosystem conditions have the lowest scores, which are primarily attributed to the water eutrophication problem. Yet the terrestrial ecosystem is assessed to be in better condition since topographic backgrounds and landscape diversity are at higher levels. Based on the work carried out, it is suggested that pollutant emission be controlled to improve water quality and agricultural development around Ge Lake (the largest lake in Wujin District) be reduced

Divide and Slide: Layer-Wise Refinement for Output Range Analysis of Deep Neural Networks

In this article, we present a layer-wise refinement method for neural network output range analysis. While approaches such as nonlinear programming (NLP) can directly model the high nonlinearity brought by neural networks in output range analysis, they are known to be difficult to solve in general. We propose to use a convex polygonal relaxation (overapproximation) of the activation functions to cope with the nonlinearity. This allows us to encode the relaxed problem into a mixed-integer linear program (MILP), and control the tightness of the relaxation by adjusting the number of segments in the polygon. Starting with a segment number of 1 for each neuron, which coincides with a linear programming (LP) relaxation, our approach selects neurons layer by layer to iteratively refine this relaxation. To tackle the increase of the number of integer variables with tighter refinement, we bridge the propagation-based method and the programming-based method by dividing and sliding the layerwise constraints. Specifically, given a sliding number s, for the neurons in layer l, we only encode the constraints of the layers between l - s and l. We show that our overall framework is sound and provides a valid overapproximation. Experiments on deep neural networks demonstrate significant improvement on output range analysis precision using our approach compared to the state-of-the-art

POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems

We propose POLAR, a \textbf{pol}ynomial \textbf{ar}ithmetic framework that leverages polynomial overapproximations with interval remainders for bounded-time reachability analysis of neural network-controlled systems (NNCSs). Compared with existing arithmetic approaches that use standard Taylor models, our framework uses a novel approach to iteratively overapproximate the neuron output ranges layer-by-layer with a combination of Bernstein polynomial interpolation for continuous activation functions and Taylor model arithmetic for the other operations. This approach can overcome the main drawback in the standard Taylor model arithmetic, i.e. its inability to handle functions that cannot be well approximated by Taylor polynomials, and significantly improve the accuracy and efficiency of reachable states computation for NNCSs. To further tighten the overapproximation, our method keeps the Taylor model remainders symbolic under the linear mappings when estimating the output range of a neural network. We show that POLAR can be seamlessly integrated with existing Taylor model flowpipe construction techniques, and demonstrate that POLAR significantly outperforms the current state-of-the-art techniques on a suite of benchmarks

ReachNN: Reachability Analysis of Neural-Network Controlled Systems

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for a small subset of inputs. To quantify the error introduced by abstraction, we provide both theoretical error bound estimation based on the theory of Bernstein polynomials and more practical sampling based error bound estimation, following a tight Lipschitz constant estimation approach based on forward reachability analysis. Compared with previous methods, our approach addresses a much broader set of neural networks, including heterogeneous neural networks that contain multiple types of activation functions. Experiment results on a variety of benchmarks show the effectiveness of our approach

Investigation into the dynamic change pattern of the stress field during integral fracturing in deep reservoirs

Deep reservoirs have high temperature, high pressure, and high stress. The development of such resources is high cost. Integral fracturing applies one-time well displacement, batch drilling, and batch fracturing. Multiple wells are stimulated with zipper fracturing. It can avoid the interference of the well drilling and fracturing. In this way, the spatial stresses can be utilized to generate the complex fracture network. The dynamic change pattern of the stress field is of great value for the design of integral fracturing. Based on the displacement discontinuity method (DDM) and the fracture mechanics criteria, a whole fracture propagation program is developed to calculate the spatial stress distribution and the whole fracture geometry. The reliability of the program is verified against the classical analytical solutions. Based on the program, this work systematically investigates the effects of the fracture length, the fracturing sequence, the fracture distribution mode, and the injection pressure on the stress field. The main conclusions are as follows: 1) When the fracture half-length is 150 m and the well spacing is 300 m, the staggered fracture distribution mode can ensure uniform fracture propagation and realize the active utilization of inter-well stress field; 2) Compared with the relative fracture distribution mode, the staggered fracture distribution mode is less susceptible to the stress field induced by the adjacent hydraulic fractures, hydraulic fractures tend to propagate along the direction of the maximum horizontal principal stress; 3) The stress field is highly influenced by the in-fracture fluid pressure. The stress interference is stronger with a greater fluid injection pressure and a higher fracture deflection angle will be obtained. It can enhance the fracture propagation resistance and increase the stress value. This work discovers the stress change pattern and lays out a solid foundation for the optimization of the integral fracturing

ARCH-COMP22 category report: Artificial intelligence and neural network control systems (AINNCS) for continuous and hybrid systems plants

This report presents the results of a friendly competition for formal verification of continuous and hybrid systems with artificial intelligence (AI) components. Specifically, machine learning (ML) components in cyber-physical systems (CPS), such as feedforward neural networks used as feedback controllers in closed-loop systems are considered, which is a class of systems classically known as intelligent control systems, or in more modern and specific terms, neural network control systems (NNCS). We more broadly refer to this category as AI and NNCS (AINNCS). The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2022. In the fourth edition of this AINNCS category at ARCH-COMP, four tools have been applied to solve 10 different benchmark problems. There are two new participants: CORA and POLAR, and two previous participants: JuliaReach and NNV. The goal of this report is to be a snapshot of the current landscape of tools and the types of benchmarks for which these tools are suited. The results of this iteration significantly outperform those of any previous year, demonstrating the continuous advancement of this community in the past decade.</jats:p