Intelligent Fault Diagnosis for Large-Scale Rotating Machines Using Binarized Deep Neural Networks and Random Forests

Recently, deep neural network (DNN) models work incredibly well, and edge computing has achieved great success in real-world scenarios, such as fault diagnosis for large-scale rotational machinery. However, DNN training takes a long time due to its complex calculation, which makes it difficult to optimize and retrain models. To address such an issue, this work proposes a novel fault diagnosis model by combining binarized DNNs (BDNNs) with improved random forests (RFs). First, a BDNN-based feature extraction method with binary weights and activations in a training process is designed to reduce the model runtime without losing the accuracy of feature extraction. Its generated features are used to train an RF-based fault classifier to relieve the information loss caused by binarization. Second, considering the possible classification accuracy reduction resulting from those very similar binarized features of two instances with different classes, we replace a Gini index with ReliefF as the attribute evaluation measure in training RFs to further enhance the separability of fault features extracted by BDNN and accordingly improve the fault identification accuracy. Third, an edge computing-based fault diagnosis mode is proposed to increase diagnostic efficiency, where our diagnosis model is deployed distributedly on a number of edge nodes close to the end rotational machines in distinct locations. Extensive experiments are conducted to validate the proposed method on the data sets from rolling element bearings, and the results demonstrate that, in almost all cases, its diagnostic accuracy is competitive to the state-of-the-art DNNs and even higher due to a form of regularization in some cases. Benefited from the relatively lower computing and storage requirements of BDNNs, it is easy to be deployed on edge nodes to realize real-time fault diagnosis concurrently.
Note to Practitioners
—Rotating machines, such as engines and motors, are the cornerstones of the modern industry. Edge computing is an emerging computing paradigm where computation is performed on the edges of networks rather than on the central cloud, thereby reducing system response time, transmission overhead, storage space, and computation resources of the cloud. Motivated by the high demand on computation for deploying DNN models and lower computation complexity for running BDNN models and easiness for large-scale deployment of BDNNs, an edge computing-based method for real-time fault diagnosis of rotating machines is proposed. First, we design a BDNN-based feature extractor to decrease the amount of computation and speed up a diagnosis processes. Then, the resulting binary features are fed to train an RF-based classifier, where we use ReliefF instead of Gini index when training a random forest model to further improve the proposed method’s diagnostic accuracy. Finally, a novel cloud-edge collaborative computing-based fault diagnostic mode is presented, where the model trained from the central cloud is deployed on the edge computing devices distributed in large-scale scenarios to realize real-time fault diagnosis. Experiment results show that the proposed method can maintain the desired accuracy but greatly enhance the diagnosis speed when deployed on the edge nodes near end physical machines. It is easily extended and used for fault detection in many industrial sectors.