一个不确定性意识的原型学习框架,具有开放世界的半监督故障诊断结构约束.
Lei Chen1, Haoyan Dong1, Shuaijie Chen1
1Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China; School of Information and Intelligent Science, Donghua University, Shanghai 201620, China.
ISA transactions
|December 16, 2025
概括
本研究介绍了OpenUPS,一个用于工业故障诊断的不确定性意识框架. 它有效地处理未知的故障类型和有限的数据,防止原型崩并提高集群精度.
科学领域:
- 工业过程监控 工业过程监控
- 机器学习用于故障检测和检测.
- 半监督学习 半监督学习
背景情况:
- 现实世界的工业故障诊断与未知的故障类型和稀缺的标记数据作斗争.
- 现有的方法经常面临原型崩和不可靠的集群,阻碍性能.
- 解决这些局限性对于强大的工业监测系统至关重要.
研究的目的:
- 为开放世界半监督故障诊断提出一个具有结构约束的不确定性意识的原型学习框架.
- 在复杂的工业环境中提高故障诊断系统的准确性和适应性.
- 在有限的数据场景中克服原型崩和不可靠的集群的挑战.
主要方法:
- 开发了一个包含结构约束的不确定性意识原型学习框架 (OpenUPS).
- 利用基于简单的等角紧框架的原型,以实现统一的分布和最大限度地分离班级中心.
- 实施了一个不确定性意识的对比策略,用于适应性选择有信息的未标记数据对.
主要成果:
- 开放UPS有效地防止原型崩,即使有有限的标记数据.
- 不确定性意识的对比策略确保了已知的类的强有力的对齐和新型故障的逐步聚类.
- 在田纳西伊斯曼工艺和聚乙烯化工艺上的实验验证证证了比现有方法更优越的性能.
结论:
- OpenUPS显示出强大的通用化和适应性,用于开放世界的工业故障诊断.
- 拟议的框架为处理未知的故障类型和有限的数据提供了一个强大的解决方案.
- 这种方法显著提高了工业环境中半监督学习的能力.
相关概念视频
Stereotype Content Model
15.3K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.3K
Propagation of Uncertainty from Systematic Error
1.2K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.2K
Structural Classification of Joints
6.9K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
A fibrous joint is where the adjacent bones are united by fibrous connective...
6.9K
Statically Indeterminate Problem Solving
666
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
666
Concepts and Prototypes
477
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
477
Propagation of Uncertainty from Random Error
1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K

