With the increasing development of artificial intelligence (AI) technologies, deep learning-driven approaches have been widely applied to predicate different machinery failures. One key challenge of failure prediction is to collect sufficient data, especially data of various failure types, to train the data-driven models. Existing studies focus on using transfer learning to transfer knowledge across machines or domains, but not across failure types. In this study, we hypothesise that knowledge about failure among similar failure types is transferable. Should the hypothesis hold, companies may no longer require a large amount of all types of failure data for predictive maintenance. This will increase the companies’ overall implementation feasibility and productivity gains. We tested our hypothesis on knowledge transferability for failure prediction in an experiment performed on rotating machinery with vibration signals. During the experiment, we first calibrated the performance of the trained deep neural network in each impending failure type. Then, we leveraged the architecture and hyperparameters of the neural network model trained from one type of failure as the pre-trained model for knowledge transfer. The pre-trained model is fine-tuned with data from another type of failure of the same machine. After that, we compared the performance of the neural network model to predict the second type of failure before and after knowledge transfer. Results showed that transferring knowledge obtained from one type of failure could vastly improve the performance of predicting another type of failure, which may not have sufficient data to train a good prediction model. This result implies that predictive analytics can apply parameter-based deep transfer learning (TL) to address the challenge of insufficient data on all types of machine failures for failure prediction.

随着人工智能 (AI) 技术的不断发展，深度学习驱动的方法已被广泛应用于预测不同的机械故障。故障预测的一个关键挑战是收集足够的数据，尤其是各种故障类型的数据，以训练数据驱动的模型。现有的研究侧重于使用迁移学习来跨机器或跨域迁移知识，而不是跨故障类型。在这项研究中，我们假设类似故障类型中有关故障的知识是可转移的。如果假设成立，公司可能不再需要大量所有类型的故障数据来进行预测性维护。这将增加公司的整体实施可行性和生产力收益。我们在具有振动信号的旋转机械上进行的实验中测试了我们关于故障预测的知识可转移性的假设。在实验过程中，我们首先校准了训练好的深度神经网络在每种即将发生的故障类型中的性能。然后，我们利用从一种故障类型训练的神经网络模型的架构和超参数作为知识转移的预训练模型。预训练模型使用来自同一机器的另一种类型故障的数据进行微调。之后，我们比较了神经网络模型在知识转移前后预测第二类故障的性能。结果表明，转移从一种故障中获得的知识可以大大提高预测另一种故障的性能，它可能没有足够的数据来训练一个好的预测模型。这一结果意味着预测分析可以应用基于参数的深度迁移学习 (TL) 来解决所有类型的机器故障数据不足的挑战，无法进行故障预测。