多目标深度信念网络集成：提升工业应用中RUL估计的效率与可靠性

需积分: 9 178 浏览量更新于2023-05-27 收藏 3.24MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

"本文主要探讨了深度信念网络（Deep Belief Networks, DBN）在剩余使用寿命（Remaining Useful Life, RUL）估计中的多目标优化应用，发表于2017年10月的《神经网络与学习系统》(IEEE Transactions on Neural Networks and Learning Systems)第28卷第10期。随着工业应用对安全、效率和可靠性日益重视，基于条件的维护（Condition-Based Maintenance, CBM）策略成为首选，而 prognostics（预测维护）则是实现CBM的关键，其核心任务就是准确估计设备的RUL。传统的神经网络方法在RUL估计上取得了显著成果，但它们的性能往往受到手工设计特征和人为设定参数的影响。为了克服这些问题，研究者提出了一个多目标深度信念网络融合（Multiobjective Deep Belief Networks Ensemble, MODBNE）方法。MODBNE结合了多目标进化算法和传统的DBN训练技术，旨在同时优化多个DBN模型的准确性（accuracy）和多样性（diversity），这两个目标常常存在冲突。通过这种方法，研究团队能够进化出一组既能保持高预测精度又能具有不同特性的DBN，从而提高整个系统的鲁棒性和整体性能。具体来说，MODBNE的工作流程包括：首先，利用多目标优化算法来引导DBN的演化过程，这涉及网络结构、权重以及超参数的自适应调整；其次，每个DBN会根据不同的学习策略和初始化条件进行训练，形成一个多样化的网络群体；最后，通过集成这些DBN的预测结果，通过加权平均或投票机制提高RUL估计的稳定性和准确性。这种方法不仅提升了RUL估计的性能，还减少了对人工干预的依赖，为工业环境中的复杂系统维护提供了有力的支持。" 这个研究在提高深度学习在预测性维护领域的实用性和有效性方面具有重要意义，展示了将多目标优化理论与深度学习技术相结合的创新思路，对于推动未来智能维护系统的研发具有深远影响。

资源详情

资源推荐

2308 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 28, NO. 10, OCTOBER 2017

bearing and electric cooling fan. Fink et al. [26] proposed

a multilayer feed-forward NN with multivalued neurons

approach to deal with the reliability and degradation time-

series prediction problem, and conducted a case study on

predicting the degradation of a railway turnout system.

Many existing data-driven prognostic approaches involve

one single model which can hardly maintain good gener-

alization performance across various prognostic scenarios,

especially when this model is well conﬁgured for a certain sce-

nario. Moreover, most approaches employ handcrafted features

which can hardly adapt to different prognostic applications.

In this paper, an ensemble of DBNs is used to address

these two issues. We propose a multiobjective evolutionary

ensemble learning framework incorporated with the traditional

DBN training technique to evolve multiple DBNs subject to

accuracy and diversity as two conﬂicting objectives. All of the

eventually evolved DBNs are combined to establish an ensem-

ble model for RUL prediction, where combination weights

are optimized via DE using a task-oriented objective. Two

prerequisites required by our proposed method include the

following:

1) the maximally allowed system degradation level (i.e., the

threshold of system failure);

2) the relevant historical data (sensor data, environmental

and/or operational parameters, and so on) of the system.

Based on historical data, prognostic models can be established

to estimate the RUL.

B. Ensemble Learning

Many works [36]–[39] have demonstrated that a single

method can seldom perform consistently well across a

variety of applications. An ensemble of multiple models

(i.e., base learners) with an appropriate combination of

individual models’ outputs can take advantage of each

ensemble member so as to improve generalization. Although

an ensemble may perform better than any of its members,

it is not easy to construct a good ensemble. The most crucial

task is to obtain base learners which individually outperform

random guessing and meanwhile are less correlated with

each other in terms of training errors [40]. The previous

works [41], [42] indicated that an ideal ensemble should

consist of base learners which can generate smaller errors and

meanwhile have good diversity among themselves. Increasing

diversity among base learners is a way to pursue better gen-

eralization. Finding an appropriate combination of diversiﬁed

ensemble members is another key task in ensemble learning.

Ensemble methods have also been applied to improve the

ability of handling noisy sensor data and complex relations

underlying the data. Opitz and Maclin [43] had shown that

good accuracy and diversity of a base learner are sufﬁcient

conditions to enable an ensemble to outperform any of its

members. However, these two objectives are often conﬂict-

ing [44]. As a result, MOEAs [45]–[50] can provide a solution.

Existing works on MOEAs-based ensembles include negative

correlation learning (NCL) [51], memetic Pareto artiﬁcial

NN [52], [53], and diverse and accurate ensemble learning

algorithm [44]. In fact, many existing ensemble prognostic

Fig. 1. Architecture of a DBN composed of three stacked RBMs, where

v represents the input layer, h

(i = 1,...,3) denote three hidden layers,

and w

(i = 1,...,3) are connection weight matrices in three RBMs.

approaches only rely on one single objective to train their

ensemble members, e.g., mean square error [30], [31], [54],

root mean square error (RMSE) [15], and PHM08 score [4],

[8], [15], [31], which may lead to undesirable generalization

performance. In this paper, we use accuracy and diversity as

two conﬂicting objectives to train the base learners of the

ensemble.

C. Deep Belief Networks

In recent years, DBNs have become one of the most active

research topics [55] in machine learning and been successfully

applied in a variety of ﬁelds, such as computer vision [56],

acoustic classiﬁcation [57], phoneme recognition [58], and

natural language processing [59]. These success stories reveal

that DBNs are capable of learning the most suitable feature

representations from data. This paper aims to explore the

application of DBNs in prognostics which has been seldom

studied in the ﬁeld of prognostics.

A majority of traditional data-driven prognostic approaches

need to ﬁrst extract (and select) a set of handcrafted features

and then utilize these features (in the form of a feature vector)

as the input of the learning model. However, such approaches

are usually unable to capture more relevant and useful feature

representations. For complex engineering systems, features

may have some hierarchical relations. A single-hidden-layer

NN may not be able to capture such relations. Furthermore,

traditional NNs are very inefﬁcient to be trained when having

a large amount of hidden neurons. Moreover, many hidden

layers may not necessarily lead to better performance due to

the dilution of the correction signal when using BP [22], [23].

Therefore, traditional NNs-based approaches usually constrain

the network to contain one or two hidden layers.

The DBN is capable of extracting a hierarchy of features,

where features at higher network levels are usually more

relevant to the ultimate task. It is composed of stacked

restricted Boltzmann machines (RBMs) which can be trained

via contrastive divergence [21] in an unsupervised manner.

1) Restricted Boltzmann Machine: The RBM [60] is a two-

layer undirected graph model, which is typically trained in an

unsupervised manner. It is composed of one visible layer and

one hidden layer with no connections allowed between nodes

at the same layer.

2) Deep Belief Network: The DBN was proposed by

Hinton et al. [21], which is composed of several stacked

RBMs [60], as shown in Fig. 1. It typically involves layerwise

剩余12页未读，继续阅读

powernature001

粉丝: 0
资源: 3

会员权益专享

多目标深度信念网络集成：提升工业应用中RUL估计的效率与可靠性

Deep Belief Network深度信念网络（DBN的Matlab代码）

深度学习--深度信念网络(Deep-Belief-Network)

Deep_Belief_nets

deep belief network的Matlab代码

Hinton首次提出深度学习的概念是在Reduc the dimensionality of data with neural networks还是 a fast learning algorithm for deep belief nets中？

python keras

Deep Learning Toolbox

deep belief network 用于时间序列分类的matlab代码

deep belief network

ENVI5.6深度学习用的什么算法

List deep learning

找几篇关于深度学习轴承监测诊断的案例、优缺点分析的近5年的文献

请给我找几篇有关神经网络的文献

通过原始数据去预测接下来一段时间的数据，可以有哪些预测方法，要求这些预测方法要是当下最新颖，预测准确度最高的方法，至少要给出10种以上的预测方法，预测方法可以是国外的小众的预测方法

无监督CNN分类算法有哪些

常见的深度学习算法有哪些，哪个最简单最容易使人理解学习

rank-based stochastic pooling

神经网络分为哪几类，并举例各类的典型网络

深度信念网络有什么优势

上述列举的所有算法和技术，请重新编辑整理一下，要去除重复的算法。

会员权益专享

最新资源