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In order to solve the problem of evaluating performance degradation of rolling bearing, this paper proposes a model containing both Alternating Direction Method of Multipliers (ADMM) and Convolutional Sparse Combination Learning. We first introduce the model during learning phase and test phase. Finally, we do an experiment on a database provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati. From experimental results, the proposed model is feasible. 1. Model Algorithm 1.1 Learning Phase The learning algorithm is named as Combination Learning because Dictionary Set is a Combination of sub-dictionary. The algorithm of Combination Learning learns from Training Set to generate Dictionary Set by iterative method. During each iteration, we learn to generate a sub-dictionary that can cover as many training samples as possible, and then let residual samples which can’t be covered by the existing sub-dictionary form a new Training Set for the next iteration. Assume that the current state is in the m-th iteration. The convolutional sparse coding can be stated as a bi-convex problem

Performance Degradation Assessment of Rolling Bearing Based on both ADMM and Sparse Combination Learning