**1. Introduction**

With the further development of globalization, automated container terminals (ACTs) are increasingly widespread. The automatic loading and unloading of containers by ACTs ensure the orderly flow of goods. This plays an important role in the globalization of the economy. As an integral part of the ACT, the reliable working condition of the port crane ensures the efficient operation of the entire terminal. The gearbox of the port crane, as an important power component, works for long periods of time and under heavy loads. A reliable condition monitoring and fault diagnosis system for the port crane gearbox is essential for a port crane [1,2]. The failure of a port crane can lead to port blockages and unnecessary economic losses or even cause injury or death. Therefore, it is essential to ensure that it works safely and securely. In practical scenarios, it is usually experienced experts or engineers who perform the maintenance of the equipment through their previous experience. For example, an experienced expert can determine the status of a device by tapping on it and locating faults according to the feedback signal characteristics. However, some critical equipment requires effective online monitoring so that faults can be detected and handled as soon as they occur.

The development of sensors such as vibration sensors, acoustic sensors, temperature sensors, pressure sensors, etc., can provide an effective means of obtaining information for such equipment [3–5]. This provides an effective means of detecting equipment in real time. Traditional analysis methods are mainly based on manual feature extraction of the collected signals. The methods of feature extraction for signals include time domain features, frequency domain features, and time–frequency domain features. Common features of the time domain include the mean value, standard deviation, root mean square value, peak value, shape indicator, skewness, kurtosis, crest indicator, clearance indicator, impulse indicator, etc., [6]. Frequency domain features usually refer to feature signals

**Citation:** Zhao, R.; Hu, X. An Adaptive Fusion Convolutional Denoising Network and Its Application to the Fault Diagnosis of Shore Bridge Lift Gearbox. *Machines* **2022**, *10*, 424. https://doi.org/ 10.3390/machines10060424

Academic Editors: Hongtian Chen, Kai Zhong, Guangtao Ran and Chao Cheng

Received: 23 April 2022 Accepted: 25 May 2022 Published: 26 May 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

extracted from the frequency spectrum, mainly including the mean frequency, frequency center, root mean square frequency, the standard deviation of frequency, etc. [7]. Frequency signals often better represent some of the hidden features of the signal than the time domain. The time–frequency domain features include energy entropy, which is usually extracted by wavelet transform (WT), wavelet package transform (WPT), or empirical model decomposition (EMD) [8–13].

With the development of artificial intelligence technology [14,15], machine learning methods are used to identify faults based on the features extracted, such as expert systems, ANN, and SVM [16,17]. The intelligent algorithms of the fault diagnosis model have a strong nonlinear fitting capability [18,19]. With a provided training target and an optimization algorithm, the intelligent algorithm often achieves a good diagnosis result after continuous iteration of the optimal search. However, the efficiency of signal feature extraction may have a significant impact on the diagnostic accuracy of these methods

In this paper, a 1D and 2D adaptive fusion convolutional neural network structure is proposed, while the parameters are integrated with a Kalman filter during the iterative training process. AF-CDN converts raw data into 2D data and uses the fast Fourier transform (FFT) technique to extract features from the signal. Then, the two signals are adaptively fused. At the same time, the use of Kalman filter technology can effectively eliminate the influence of noise in the raw data on the diagnostic results. The network has excellent diagnostic accuracy, while the robustness is greatly improved. Based on the historical data, we built an online condition monitoring system for port crane gearboxes. We also test our proposed algorithm on a public bearing dataset from Case Western Reserve University, and the results show that AF-CDN is well suited for different situations.

The main contributions of this paper are summarized as follows:


The subsequent sections of this manuscript are organized as follows: Section 2 presents the preliminary work. Section 3 describes the method proposed in detail. Section 4 designs experimental validation for the proposed algorithms. Section 5 presents the conclusion and provides suggestions for future work
