Control Engineering Practice最新文献_第3页

Integral predictive sliding mode control for high-speed trains: A dynamic linearization and input constraint-based data-driven scheme 高速列车的积分预测滑动模式控制：基于数据驱动的动态线性化和输入约束方案

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-25 DOI: 10.1016/j.conengprac.2024.106139

Liang Zhou, Zhong-Qi Li, Hui Yang, Chang Tan

A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [

-

0.176 km/h, 0.152 km/h], while the control force and acceleration are within [

-

55.7 kN, 44.8 kN] and [

-

0.564 m/s

^{2}

, 0.496 m/s

^{2}

], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.

具有高可靠性和优异跟踪性能的控制方案对于高速列车（HST）的自动运行至关重要。本研究针对多动力单元高速列车提出了一种新型离散时间数据驱动预测滑模控制（DDPSMC）方案。首先，设计了一个非线性积分终端滑模曲面来替代传统的线性滑模函数，从而实现了系统误差的快速收敛并缓解了颤振。然后，将后退视界优化集成到预测控制中，使预测的滑动模式状态遵循预定义的连续收敛法则的预期轨迹。这一方案使系统能够获得更高的输出误差精度，并明确处理输入约束。此外，为了增强鲁棒性，还引入了参数更新法则和扰动延迟估计算法，以分别计算控制增益和总不确定性。最后，在实验室环境中使用 CRH380A HST 仿真实验平台对所提出的控制方案进行了对比测试。仿真结果表明，在提出的控制方案下，HST 各动力装置的速度误差范围在 [-0.176 km/h, 0.152 km/h] 以内，控制力和加速度分别在 [-55.7 kN, 44.8 kN] 和 [-0.564 m/s2, 0.496 m/s2] 以内，且变化稳定，其他性能指标也优于其他比较方法。这些结果满足了列车的安全性、稳定性和正点性要求。

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引用次数: 0

Corrective variational mode decomposition to detect multiple oscillations in process control systems 纠正变分模式分解以检测过程控制系统中的多重振荡

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-24 DOI: 10.1016/j.conengprac.2024.106123

Songhua Liu , Xun Lang , Jiande Wu , Yufeng Zhang , Cong Lei , Hongye Su

Monitoring oscillatory behavior in industrial control systems is essential to ensure process safety and enhance productivity, but unfortunately, current decomposition-based monitoring methods struggle to extract and accurately detect multiple oscillations. This struggle is primarily due to the level of noise susceptibility in current monitoring methods and the intermittent nature of multiple oscillations in industrial control systems. As a potential solution, variational mode decomposition (VMD) shows promising advantages in processing non-stationary industrial signals with a significant amount of interference. Nevertheless, improperly configuring the number of modes in the VMD can lead to mode mixing and degrade the oscillation extraction accuracy. To overcome this challenge, we propose a corrective VMD approach that automatically adjusts the mode number to create a robust and automated framework for monitoring multiple oscillations. Our framework excels in detecting oscillatory behavior and quantifying the number of oscillations, even in the presence of noisy, intermittent, and irregular disturbances. To validate its effectiveness and practicality, we applied the framework to both a benchmark industrial dataset and a self-constructed industrial dataset, comparing its performance against state-of-the-art oscillation detection methods. The framework demonstrated superior accuracy, achieving 93.90% in detecting oscillations and 85.37% in quantifying the number of oscillations within the benchmark dataset, with similarly excellent results observed in the self-constructed industrial dataset.

监测工业控制系统中的振荡行为对于确保过程安全和提高生产率至关重要，但遗憾的是，目前基于分解的监测方法难以提取和准确检测多重振荡。造成这种困难的主要原因是目前的监测方法对噪声的敏感程度以及工业控制系统中多重振荡的间歇性。作为一种潜在的解决方案，变分模式分解（VMD）在处理具有大量干扰的非稳态工业信号方面显示出了很好的优势。然而，不适当地配置 VMD 中的模式数会导致模式混合，降低振荡提取的精度。为了克服这一难题，我们提出了一种自动调整模式数的纠正 VMD 方法，以创建一个用于监测多重振荡的稳健而自动化的框架。我们的框架在检测振荡行为和量化振荡数量方面表现出色，即使在存在噪声、间歇性和不规则干扰的情况下也是如此。为了验证其有效性和实用性，我们将该框架应用于基准工业数据集和自建工业数据集，并将其性能与最先进的振荡检测方法进行了比较。该框架表现出了极高的准确性，在基准数据集中的振荡检测率达到了 93.90%，在量化振荡数量方面达到了 85.37%，在自建工业数据集中也取得了类似的优异成绩。

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引用次数: 0

Hierarchical grouping and visualization of correlated alarms using time-augmented word embedding 利用时间增强词嵌入对相关警报进行分层分组和可视化处理

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-24 DOI: 10.1016/j.conengprac.2024.106130

Aliakbar Davoodi, Ahmad W. Al-Dabbagh

In industrial processes, a large number of alarms displayed on human–machine interface screens may overwhelm human operators. This prevents them from taking appropriate corrective actions in a timely manner. Therefore, this paper proposes a three-stage computational procedure for grouping and visualizing correlated alarms, such that root-cause abnormalities can be more easily identified by the human operators. In the first stage, using a word embedding-based approach, alarm tags are transformed into real-valued vectors, where time stamps of the alarms are used rather than their order of occurrence. In the second stage, a multi-level density-based clustering approach is utilized to group correlated alarms hierarchically. In the third stage, a hierarchical visualization approach is developed to display alarm groups to human operators, which depicts hierarchical and statistical information. The implementation and effectiveness of the three-stage computational procedure are demonstrated using an alarm dataset generated for the benchmark Tennessee Eastman process system.

在工业流程中，人机界面屏幕上显示的大量警报可能会让操作员不知所措。这使他们无法及时采取适当的纠正措施。因此，本文提出了一种分三个阶段的计算程序，用于对相关警报进行分组和可视化，从而使人类操作员更容易识别异常的根本原因。在第一阶段，使用基于词嵌入的方法，将警报标签转换为实值向量，其中使用警报的时间戳而不是其发生顺序。在第二阶段，利用基于密度的多级聚类方法将相关警报分级分组。在第三阶段，开发了一种分级可视化方法，用于向人工操作员显示警报组，其中描述了分级和统计信息。使用为基准田纳西伊士曼流程系统生成的警报数据集，演示了三阶段计算程序的实施和有效性。

引用次数: 0

Design and experimental validation of eco-driving system for connected and automated electric vehicles 互联和自动驾驶电动汽车生态驾驶系统的设计与实验验证

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-24 DOI: 10.1016/j.conengprac.2024.106132

Xi Luo , Yifan Cheng , Jinlong Hong , Shiying Dong , Xiaoxiang Na , Bingzhao Gao , Hong Chen

To address range anxiety in electric vehicles (EVs), this paper presents an eco-driving add-on system implemented on a production EV, with comparative field experiments conducted in real-world traffic conditions. The proposed eco-driving system integrates a predictive cruise control (PCC) strategy to effectively utilize connected information, such as road geometry and preceding vehicle behaviors. For real-time implementation, a fast PCC algorithm coupled with the bisection method, warm-start, and improved iterative transversality condition is introduced. Numerical simulations validate the effectiveness of the proposed scheme, achieving an energy-saving effect of approximately 2%. Subsequently, field experiments were conducted in scenarios including smooth-flowing highways and congested urban expressways using a production EV. Compared to the baseline, which consists of the existing cruise control strategy of EVs and the experienced human drivers, our proposed scheme achieves energy savings of approximately 2.2% on highways and 2.6% on urban expressways.

为解决电动汽车（EV）的续航焦虑问题，本文介绍了一种在量产电动汽车上实施的生态驾驶附加系统，并在实际交通条件下进行了现场对比实验。拟议的生态驾驶系统集成了预测巡航控制（PCC）策略，可有效利用道路几何形状和前车行为等相关信息。为实现实时性，引入了一种快速 PCC 算法，该算法结合了分段法、热启动和改进的迭代横向条件。数值模拟验证了所提方案的有效性，实现了约 2% 的节能效果。随后，使用量产电动汽车在畅通的高速公路和拥堵的城市快速路等场景中进行了实地实验。与基线（包括现有的电动汽车巡航控制策略和经验丰富的人类驾驶员）相比，我们提出的方案在高速公路上实现了约 2.2% 的节能效果，在城市快速路上实现了 2.6% 的节能效果。

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引用次数: 0

Practical solution for attenuating industrial heavy vehicle vibration: A new gain-adaptive coordinated suspension control system 减轻工业重型车辆振动的实用解决方案：新型增益自适应协调悬挂控制系统

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-23 DOI: 10.1016/j.conengprac.2024.106125

Yukun Lu , Ran Zhen , Yegang Liu , Jiaming Zhong , Chen Sun , Yanjun Huang , Amir Khajepour

Prioritizing the improvement of truck driver’s ride comfort is crucial for the health and well-being of drivers, driver retention, safety, overall productivity, regulatory compliance, and customer satisfaction. As a solution, adaptive suspension systems are developed to optimize suspension performances. In this study, a novel integrated Skyhook-LQR algorithm is introduced, which aims to simultaneously improve the sprung mass dynamics in vertical, pitch, and roll directions. Most importantly, it requires affordable computational cost and can be processed on an automotive-grade microcontroller. Besides, it is impossible to find one set of optimum control gains for rapid-changing disturbances since the vehicle may be driven on various road surfaces. A gain-adaptive algorithm is developed to intelligently adjust the LQR’s output penalty matrix Q according to onboard sensor measurements to fill this gap. The performance and effectiveness of the proposed techniques are experimentally examined based on a scaled-down cab-over-engine model and a Stewart Platform. The vehicle responses and disturbance inputs are measured by two 6-axis IMUs and four height sensors, and all the messages are transmitted through the CAN Bus. The unmeasurable states are estimated by a Kalman filter observer. The experimental results validated that the integrated Skyhook-LQR has excellent potential in suspension coordinated control, which significantly optimizes ride quality. Meanwhile, the gain-adaptive algorithm detected vehicle motions and provided efficient gain scheduling decisions, by which the undesired vibrations and shocks were further attenuated to some extent.

优先改善卡车司机的驾乘舒适度对于司机的健康和福祉、司机的留任、安全性、整体生产率、法规遵从性和客户满意度至关重要。作为一种解决方案，开发了自适应悬架系统来优化悬架性能。在本研究中，引入了一种新颖的集成式 Skyhook-LQR 算法，旨在同时改善垂直、俯仰和滚动方向的弹簧质量动态。最重要的是，该算法计算成本低廉，可在汽车级微控制器上进行处理。此外，由于车辆可能会在不同的路面上行驶，因此不可能针对快速变化的干扰找到一套最佳控制增益。为弥补这一不足，我们开发了一种增益自适应算法，可根据车载传感器测量结果智能调整 LQR 的输出惩罚矩阵 Q。基于缩小的驾驶室上方发动机模型和斯图尔特平台，对所提技术的性能和有效性进行了实验检验。车辆响应和干扰输入由两个 6 轴 IMU 和四个高度传感器测量，所有信息均通过 CAN 总线传输。不可测量的状态由卡尔曼滤波观测器进行估计。实验结果验证了集成式 Skyhook-LQR 在悬架协调控制方面具有出色的潜力，可显著优化行驶质量。同时，增益自适应算法可检测车辆运动并提供有效的增益调度决策，从而在一定程度上进一步减弱了不期望的振动和冲击。

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引用次数: 0

Adaptive synchronous tracking control for n-PPPS redundantly actuated distributed parallel manipulators with dynamic uncertainties 具有动态不确定性的 n-PPPS 冗余致动分布式并联机械手的自适应同步跟踪控制

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-22 DOI: 10.1016/j.conengprac.2024.106135

Sen Liang , Bing Han , Xinfeng Wang , Xinfang Zhou , Qiang Fang , Yanding Wei

Redundantly actuated distributed parallel manipulators (RADPMs) are widely used for posture alignment and assembly of large-scale components. The structural characteristics of multiple redundant actuation chains not only possess potential advantages, but also bring about challenges for multi-joint coordinated motion. To address the synchronization control issue of the system with dynamic uncertainties, a novel adaptive synchronous tracking control (ASTC) scheme is proposed to realize high-precision trajectory tracking and coordination performance simultaneously. In the proposed ASTC scheme, a synchronization error is first introduced to depict the coordination relationship between adjacent joints and coupled with the tracking error to form a composite error in the joint space. Based on the defined errors, a dual-space adaptation law is proposed through the linear parameterized expression of the system dynamic model to obtain feedforward compensation for dynamics. Additionally, in order to restrain the influence of inevitable external disturbances, a robust control compensation term is introduced to improve the disturbance rejection ability. Moreover, the stability of the entire closed-loop system is proved by utilizing the Lyapunov theory. Finally, simulation and experiments are conducted on an actual 4-PPPS RADPM, and the comparative results demonstrate that the proposed scheme can effectively improve the tracking accuracy and synchronization performance of the system.

冗余致动分布式并联机械手（RADPM）被广泛用于大型部件的姿势校准和装配。多冗余执行链的结构特点不仅具有潜在优势，同时也为多关节协调运动带来了挑战。针对具有动态不确定性的系统的同步控制问题，提出了一种新型自适应同步跟踪控制（ASTC）方案，以同时实现高精度轨迹跟踪和协调性能。在所提出的 ASTC 方案中，首先引入同步误差来描述相邻关节之间的协调关系，并与跟踪误差耦合形成关节空间中的复合误差。根据定义的误差，通过系统动态模型的线性参数化表达，提出了双空间适应法则，从而获得动态的前馈补偿。此外，为了抑制不可避免的外部干扰的影响，还引入了鲁棒控制补偿项，以提高干扰抑制能力。此外，利用 Lyapunov 理论证明了整个闭环系统的稳定性。最后，在实际的 4-PPPS RADPM 上进行了仿真和实验，对比结果表明所提出的方案能有效提高系统的跟踪精度和同步性能。

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引用次数: 0

WOA-tuned supertwisted synergetic control of multipurpose on-board charger for G2V/V2G/V2V operational modes of electric vehicles 针对电动汽车的 G2V/V2G/V2V 运行模式，对多功能车载充电器进行 WOA 调谐的超扭曲协同控制

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-22 DOI: 10.1016/j.conengprac.2024.106136

Hafiz Mian Muhammad Adil, Hassan Abbas Khan

On-board chargers within electric vehicles (EVs) must efficiently manage grid-to-vehicle (G2V), vehicle-to-grid (V2G), and vehicle-to-vehicle (V2V) modes for sustainable EV operation. This paper introduces a modified hybrid nonlinear control approach that utilizes the whale optimization algorithm-tuned supertwisted synergetic (WOA-ST-syn) technique for a multipurpose on-board charger (MP-OBC). The whale optimization algorithm(WOA) adjusts the parameters of supertwisted synergetic controller using the integral time absolute error, reducing the need for exhaustive trial-and-error adjustments. The controller employs the state space model of a two-stage on-board electric vehicle charging system, ensuring stability through the Lyapunov stability criterion. Simulations in MATLAB/Simulink evaluate the performance of the proposed controller across various operational modes, testing robustness against varying load currents and mode-switching conditions. Results indicate significant improvements over state-of-the-art nonlinear controllers, with minimal chattering, shortest rise time (0.0007 s for AC-DC, 1.5520 s for DC-DC), fastest settling time (0.0447 s for AC-DC, 2.0550 s for DC-DC), and minimal steady-state error (0.0010% for AC-DC, 0.0004% for DC-DC). Controller Hardware-in-the-Loop (C-HIL) experiments were also performed to confirm the real-time applicability of the controller.

电动汽车（EV）的车载充电器必须有效管理电网到车辆（G2V）、车辆到电网（V2G）和车辆到车辆（V2V）模式，以实现电动汽车的可持续运行。本文针对多功能车载充电器（MP-OBC）介绍了一种改进的混合非线性控制方法，该方法利用了鲸鱼优化算法调谐超扭曲协同（WOA-ST-syn）技术。鲸鱼优化算法（WOA）利用积分时间绝对误差来调整超扭曲协同控制器的参数，从而减少了反复试错调整的需要。控制器采用两级车载电动汽车充电系统的状态空间模型，通过 Lyapunov 稳定性准则确保稳定性。在 MATLAB/Simulink 中进行的仿真评估了拟议控制器在各种运行模式下的性能，测试了其在不同负载电流和模式切换条件下的稳健性。结果表明，与最先进的非线性控制器相比，该控制器的性能有了明显改善，颤振最小，上升时间最短（交流直流为 0.0007 秒，直流直流为 1.5520 秒），平稳时间最快（交流直流为 0.0447 秒，直流直流为 2.0550 秒），稳态误差最小（交流直流为 0.0010%，直流直流为 0.0004%）。还进行了控制器硬件在环 (C-HIL) 实验，以确认控制器的实时适用性。

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引用次数: 0

An improved multi-channel and multi-scale domain adversarial neural network for fault diagnosis of the rolling bearing 用于滚动轴承故障诊断的改进型多通道多尺度域对抗神经网络

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-21 DOI: 10.1016/j.conengprac.2024.106120

Yongze Jin , Xiaohao Song , Yanxi Yang , Xinhong Hei , Nan Feng , Xubo Yang

To improve the fault diagnosis accuracy of rolling bearings under diverse working conditions, an improved domain adversarial neural network is proposed, the feature extraction module is reconstructed by multi-channel and multi-scale CNN-LSTM-ECA (MMCLE) in the proposed network. The MMCLE module consists of several key components. Firstly, the multi-channel multi-scale Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are established to extract spatial features and temporal dependencies of the input data. Then, the Efficient Channel Attention (ECA) module is introduced to weight the effective feature channels. Finally, the domain adversarial training is employed to extract common features from both the source and target domains. By minimizing the domain offset between these domains, the faults of rolling bearing under diverse working conditions can be accurately diagnosed. The simulation results show that, based on the proposed MMCLE model, the domain offset issue can be effectively addressed, and the fault diagnosis accuracy can be improved for samples in the target domain under diverse working conditions. The accuracy and feasibility of the proposed method can be effectively verified.

为提高不同工况下滚动轴承的故障诊断精度，提出了一种改进的域对抗神经网络，其特征提取模块是通过多通道、多尺度 CNN-LSTM-ECA (MMCLE) 重构的。MMCLE 模块由几个关键部分组成。首先，建立多通道多尺度卷积神经网络（CNN）和长短期记忆（LSTM），以提取输入数据的空间特征和时间相关性。然后，引入高效通道注意（ECA）模块，对有效特征通道进行加权。最后，采用域对抗训练来提取源域和目标域的共同特征。通过最小化源域和目标域之间的域偏移，可以准确诊断不同工况下滚动轴承的故障。仿真结果表明，基于所提出的 MMCLE 模型，可以有效地解决域偏移问题，并提高不同工况下目标域样本的故障诊断精度。该方法的准确性和可行性得到了有效验证。

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引用次数: 0

A high-performance model predictive torque control concept for induction machines for electric vehicle applications 用于电动汽车感应机的高性能模型预测扭矩控制概念

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-19 DOI: 10.1016/j.conengprac.2024.106128

Georg Janisch , Andreas Kugi , Wolfgang Kemmetmüller

Induction machines are widely used in electric vehicles due to their high reliability and low costs. Controlling these machines to meet the high-performance demands presents a significant challenge since they are often operated at high speed and within operating ranges where magnetic saturation plays a significant role. Furthermore, specific motor parameters are not accurately known or vary during operation, e.g., due to temperature changes. Therefore, there is still a demand for control strategies to meet these demands systematically. This paper proposes a novel control strategy combining a model predictive control (MPC) concept with a fast feedback controller and a nonlinear observer. The proposed MPC strategy is based on a magnetic nonlinear model and allows for a long prediction horizon. It features high torque dynamics while ensuring energy optimality in the steady state. The results also show excellent performance for high rotational speeds and the operation at the system limits, outperforming state-of-the-art control concepts.

感应电机因其高可靠性和低成本而被广泛应用于电动汽车中。控制这些机器以满足高性能要求是一项重大挑战，因为它们通常是在高速运转和磁饱和起重要作用的工作范围内运行。此外，具体的电机参数并不准确，或者在运行过程中会发生变化，例如由于温度变化。因此，仍然需要控制策略来系统地满足这些需求。本文提出了一种结合模型预测控制（MPC）概念、快速反馈控制器和非线性观测器的新型控制策略。所提出的 MPC 策略基于磁性非线性模型，允许较长的预测范围。它具有高扭矩动态特性，同时确保稳态下的能量优化。研究结果还显示，该策略在高转速和系统极限运行时表现出色，优于最先进的控制概念。

引用次数: 0

A reusable decoder network penalized by smooth group lasso and its applications to large-scale fault diagnosis of machinery 用平滑组套索惩罚的可重复使用解码器网络及其在大规模机械故障诊断中的应用

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-17 DOI: 10.1016/j.conengprac.2024.106127

Zhiqiang Zhang, Hongji He, Shuiqing Xu, Lisheng Yin, Xueping Dong

Representation learning approaches have achieved great success in fault diagnosis of large-scale mechanical data, among which the popular auto-encoder method has developed a series of effective variants. In the existing variants, the encoder network is re-employed to encode feature representations of the data, while the decoder network is directly discarded after training, leading to a regrettable waste of computational resources. Instead of proposing advanced variants of the auto-encoder, this paper explicitly penalizes the decoder network with group lasso, thereby transforming waste into treasure. Specifically, the group lasso constrains the column vectors of the decoder network’s weight matrix at the group level, making them reusable for feature selection. Moreover, a smooth function is utilized to approximate the group lasso to prevent numerical oscillations when computing the gradients. The simulated data and experimental gear data are sequentially used to verify the effectiveness of the smooth group lasso through investigations on two representative auto-encoder variants. The results show that the decoder network penalized by smooth group lasso can be re-utilized to guide selection of a subset of key features for training a classifier, exhibiting an extraordinary feature selection capability.

表征学习方法在大规模机械数据的故障诊断中取得了巨大成功，其中流行的自动编码器方法已发展出一系列有效的变体。在现有的变体中，编码器网络被重新用于对数据的特征表示进行编码，而解码器网络则在训练后被直接丢弃，这导致了令人遗憾的计算资源浪费。本文并没有提出自动编码器的高级变体，而是通过组套索（group lasso）对解码器网络进行明确的惩罚，从而变废为宝。具体来说，组套索在组的层面上对解码器网络权重矩阵的列向量进行约束，使其可重新用于特征选择。此外，在计算梯度时，利用平滑函数来近似组套索，以防止数值振荡。通过对两个具有代表性的自动编码器变体的研究，模拟数据和实验齿轮数据依次用于验证平滑组套索的有效性。结果表明，通过平滑组套索惩罚的解码器网络可以重新用于指导选择用于训练分类器的关键特征子集，表现出非凡的特征选择能力。

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引用次数: 0