The application of cutting-edge technologies such as AI, smart sensors, and IoT in factories is revolutionizing the manufacturing industry. This emerging trend, so called smart manufacturing, is a collection of various technologies that support decision-making in real-time in the presence of changing conditions in manufacturing activities; this may advance manufacturing competitiveness and sustainability. As a factory becomes highly automated, physical asset management comes to be a critical part of an operational life-cycle. Maintenance is one area where the collection of technologies may be applied to enhance operational reliability using a machine condition monitoring system. Data-driven models have been extensively applied to machine condition data to build a fault detection system. Most existing studies on fault detection were developed under a fixed set of operating conditions and tested with data obtained from that set of conditions. Therefore, variability in a model’s performance from data obtained from different operating settings is not well reported. There have been limited studies considering changing operational conditions in a data-driven model. For practical applications, a model must identify a targeted fault under variable operational conditions. With this in mind, the goal of this paper is to study invariance of model to changing speed via a deep learning method, which can detect a mechanical imbalance, i.e., targeted fault, under varying speed settings. To study the speed invariance, experimental data obtained from a motor test-bed are processed, and time-series data and time–frequency data are applied to long short-term memory and convolutional neural network, respectively, to evaluate their performance.

人工智能，智能传感器和物联网等尖端技术在工厂中的应用正在彻底改变制造业。这种新兴趋势被称为智能制造，它是各种技术的集合，这些技术可以在制造活动条件变化的情况下实时支持实时决策。这可以提高制造业的竞争力和可持续性。随着工厂的高度自动化，实物资产管理已成为运营生命周期的关键部分。维护是可以使用机器状态监视系统来收集技术以增强操作可靠性的一个领域。数据驱动模型已广泛应用于机器状态数据，以构建故障检测系统。现有的大多数故障检测研究都是在固定的一组工作条件下进行的，并使用从该组条件下获得的数据进行了测试。因此，没有很好地报告根据从不同操作设置获得的数据得出的模型性能的差异。考虑到在数据驱动模型中更改操作条件的研究很少。对于实际应用，模型必须在可变操作条件下识别目标故障。考虑到这一点，本文的目标是通过深度学习方法研究模型对速度变化的不变性，该方法可以检测速度变化设置下的机械失衡，即目标故障。为了研究速度不变性，需要处理从电动机测试台获得的实验数据，