Predictive Maintenance for Gantry Crane Shafts: Enhancing Sensor Features With Dynamic Dual-Domain Competitive Attention GRU Autoencoder

Anomaly prediction is an important aspect of predictive maintenance for port machinery and equipment. Traditional anomaly prediction techniques process the raw time- or frequency-domain data, which makes time-series data difficult to reconstruct. In particular, time-series data with weak features, such as sensor data, are difficult to model. Furthermore, the absence of sufficient fault-labeled data for industrial processes means that classification models are difficult to train. Therefore, this study introduces an advanced temporal anomaly prediction model, which uses a gated recurrent unit (GRU) autoencoder to process time- and frequency-domain data in parallel. The feature match and temporal consistency of the data are modeled using a bidomain competitive attention module and bidirectional loss function, respectively, which reduces the mean squared error (MSE) by 15.43%. Weak fault features in the sensor data are enhanced using dynamics simulations, which address the problem of missing fault labels by using a threshold diagnostic model instead of supervised learning. Thus, the recall rate is increased by 9.04%. An attention storage pool approach is also used to mitigate the prediction decay caused by external variables. Overall, the proposed method can improve the efficiency and safety of industrial machinery by addressing the challenges involved in modeling time-series sensor data with weak features.

I. Introduction

The demand for industrial production equipment with high operational stability and efficiency is increasing owing to advancements in automation and intelligent technology. In particular, the performance of heavy machinery and equipment, such as gantry cranes and their shaft ends, directly affects the overall efficiency and safety of industrial operations. Predictive maintenance is an emerging strategy that can be used for advanced fault identification, which can prevent equipment failures, reduce downtime, and improve productivity.