Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

Cloud fraction (CF) significantly affects the short‐ and long‐wave radiation. Its realistic representation in general circulation models (GCMs) still poses great challenges in modeling the atmosphere. Here, we present a neural network‐based (NN‐based) diagnostic scheme that uses the grid‐mean temperature, pressure, liquid and ice water mixing ratios, and relative humidity to simulate the sub‐grid CF. The scheme, trained using CloudSat data with explicit consideration of grid sizes, realistically simulates the observed CF with a correlation coefficient >0.9 for liquid‐, mixed‐, and ice‐phase clouds. The scheme also captures the observed non‐monotonic relationship between CF and relative humidity and is computationally efficient, and robust for GCMs with a variety of horizontal and vertical resolutions. For illustrative purposes, we conducted comparative analyses of the 2006–2019 climatological‐mean cloud fractions among CloudSat, and simulations from the NN‐based scheme and the Xu‐Randall scheme (optimized the same way as the NN‐based scheme). The NN‐based scheme improves not only the spatial distribution of the total CF but also the cloud vertical structure. For example, the biases of too‐many high‐level clouds over the tropics and too‐many low‐level clouds over regions around 60°S and 60°N in the Xu‐Randall scheme are significantly reduced. These improvements are also found to be insensitive to the spatio‐temporal variability of large‐scale meteorology conditions, implying that the scheme can be used in different climate regimes.