Fault detection and diagnosis of electrical motors are of utmost importance
in ensuring the safe and reliable operation of several industrial systems.
Detection and diagnosis of faults at the incipient stage allows corrective
actions to be taken in order to reduce the severity of faults. The existing
data-driven deep learning approaches for machine fault diagnosis rely
extensively on huge amounts of labeled samples, where annotations are expensive
and time-consuming. However, a major portion of unlabeled condition monitoring
data is not exploited in the training process. To overcome this limitation, we
propose a foundational model-based Active Learning framework that utilizes less
amount of labeled samples, which are most informative and harnesses a large
amount of available unlabeled data by effectively combining Active Learning and
Contrastive Self-Supervised Learning techniques. It consists of a transformer
network-based backbone model trained using an advanced nearest-neighbor
contrastive self-supervised learning method. This approach empowers the
backbone to learn improved representations of samples derived from raw,
unlabeled vibration data. Subsequently, the backbone can undergo fine-tuning to
address a range of downstream tasks, both within the same machines and across
different machines. The effectiveness of the proposed methodology has been
assessed through the fine-tuning of the backbone for multiple target tasks
using three distinct machine-bearing fault datasets. The experimental
evaluation demonstrates a superior performance as compared to existing
state-of-the-art fault diagnosis methods with less amount of labeled data.

利用基于模型的主动学习和对比自监督学习技术来优化未标记的振动数据，提出了一种新的机械故障诊断方法，通过少量标记样本和大量未标记数据的有效组合，实现了比现有方法更出色的性能。

电机故障诊断的主动基础模型

Active Foundational Models for Fault Diagnosis of Electrical Motors

A majority of recent advancements related to the fault diagnosis of
electrical motors are based on the assumption that training and testing data
are drawn from the same distribution. However, the data distribution can vary
across different operating conditions during real-world operating scenarios of
electrical motors. Consequently, this assumption limits the practical
implementation of existing studies for fault diagnosis, as they rely on fully
labelled training data spanning all operating conditions and assume a
consistent distribution. This is because obtaining a large number of labelled
samples for several machines across different fault cases and operating
scenarios may be unfeasible. In order to overcome the aforementioned
limitations, this work proposes a framework to develop a foundational model for
fault diagnosis of electrical motors. It involves building a neural
network-based backbone to learn high-level features using self-supervised
learning, and then fine-tuning the backbone to achieve specific objectives. The
primary advantage of such an approach is that the backbone can be fine-tuned to
achieve a wide variety of target tasks using very less amount of training data
as compared to traditional supervised learning methodologies. The empirical
evaluation demonstrates the effectiveness of the proposed approach by obtaining
more than 90\% classification accuracy by fine-tuning the backbone not only
across different types of fault scenarios or operating conditions, but also
across different machines. This illustrates the promising potential of the
proposed approach for cross-machine fault diagnosis tasks in real-world
applications.

提出了一种基于自监督学习的神经网络模型来解决电动机故障诊断的问题，通过微调模型在不同故障情景、不同机器之间实现了超过 90％的分类准确度，具有广泛的潜力应用于现实世界中的跨机器故障诊断任务。

电机故障诊断的基础模型

Foundational Models for Fault Diagnosis of Electrical Motors

Electrical motors are the most important source of mechanical energy in the
industrial world. Their modeling traditionally relies on a physics-based
approach, which aims at taking their complex internal dynamics into account. In
this paper, we explore the feasibility of modeling the dynamics of an
electrical motor by following a data-driven approach, which uses only its
inputs and outputs and does not make any assumption on its internal behaviour.
We propose a novel encoder-decoder architecture which benefits from recurrent
skip connections. We also propose a novel loss function that takes into account
the complexity of electrical motor quantities and helps in avoiding model bias.
We show that the proposed architecture can achieve a good learning performance
on our high-frequency high-variance datasets. Two datasets are considered: the
first one is generated using a simulator based on the physics of an induction
motor and the second one is recorded from an industrial electrical motor. We
benchmark our solution using variants of traditional neural networks like
feedforward, convolutional, and recurrent networks. We evaluate various design
choices of our architecture and compare it to the baselines. We show the domain
adaptation capability of our model to learn dynamics just from simulated data
by testing it on the raw sensor data. We finally show the effect of signal
complexity on the proposed method ability to model temporal dynamics.

使用数据驱动方法建立一种编码器 - 解码器架构的神经网络模型，以对电力电机的动态进行建模，同时比较了传统神经网络模型的性能，并测试了该方法对于模拟数据的领域自适应能力和对信号复杂性的影响。