Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

We adopted actual intelligent production requirements and proposed a tiny part defect detection method to obtain a stable and accurate real-time tiny part defect detection system and solve the problems of manually setting conveyor speed and industrial camera parameters in defect detection for factory products. First, we considered the important influences of the properties of tiny parts and the environmental parameters of a defect detection system on its stability. Second, we established a correlation model between the detection capability coefficient of the part system and the moving speed of the conveyor. Third, we proposed a defect detection algorithm for tiny parts that are based on a single short detector network (SSD) and deep learning. Finally, we combined an industrial real-time detection platform with the missed detection algorithm for mechanical parts based on intermediate variables to address the problem of missed detections. We used a 0.8 cm darning needle as the experimental object. The system defect detection accuracy was the highest when the speed of the conveyor belt was 7.67 m/min.