###### Abstract  
We propose an instance-based learning approach with data augmentation and similarity evaluation to estimate the remaining useful life (RUL) of a mechanical component for health management. The publicly available PRONOSTIA datasets, which provide accelerated degradation test data for bearings, are used in our study. The challenges with the datasets include a very limited number of run-to-failure examples, no failure mode information, and a wide range of bearing life spans. Without a large number of training samples, feature engineering is necessary. Principal component analysis is applied to the spectrogram of vibration signals to obtain prognostic feature sequences. A data augmentation strategy is developed to generate synthetic prognostic feature sequences using learning instances. Subsequently, similarities between the test and learning instances can be assessed using a root mean squared (RMS) difference measure. Finally, an ensemble method is developed to aggregate the RUL estimates based on multiple similar prognostic feature sequences. The proposed approach demonstrates comparable performance with published solutions in the literature. It serves as an alternative method for solving the RUL estimation problem. b 2021 2021 18 September 2021 10 October 2021 10 October 2021 10 October  
short-term memory (LSTM) for the RUL estimation [6]. These approaches usually aim to construct a generalized prognostic model to map the underlying relationship between prognostic features and the estimated RUL based on a wide range of historic run-to-failure samples. However, in real-world applications, they have often encountered difficulties in constructing such generalized models without sufficient historic degradation samples. As one of the machine learning approaches, the instance-based learning (IBL) approach utilizes the experience gained particularly from similar historic instances to solve new problem instances, rather than building a generalized prognostic model. Usually, there are three major steps in the IBL application for RUL prediction [8, 9]:  
1. instance retrieval from a historical dataset;
2. prediction through local models based on the instances retrieved;
3. aggregation of local predictions. The instance retrieval step aims to select a set of learning instances of run-to-failure from historical datasets, whose degradation processes are deemed similar to the given test instance. Once completing the retrieval step, a local RUL prediction can be estimated by identifying the location of the current time cycle in the life cycle of each learning instance retrieved. In the aggregation step, an ensemble method is usually employed to generate the final estimation result from all local RUL predictions. In order to perform instance retrieval, similarity evaluation between a learning instance and a test instance usually adopts the Euclidean distance measure. In early IBL applications for RUL prediction, similarity measure is based on a set of attributes that characterize each instance. In an application proposed by [PERSON] et al., the RUL prediction for a given test locomotive was obtained by a fuzzy aggregation of a cluster of peers. In the cluster, each peer had a similar instance to the given locomotive in terms of a set of attributes related to the usage and maintenance history [10]. [PERSON] et al. used a similar approach to make RUL prediction for a given aircraft engine. [11]. For RUL prediction applications, existing IBL approaches usually evaluate instance similarity based on point-wise comparison of feature sequences (i.e., time series), which consists of prognostic feature vectors or health indicators (HI) constructed at every observation time cycle. For example, [PERSON] et al. created a library of degradation models using run-to-failure datasets from multiple engine units. Each of those prognostic models was characterized by HIs using a linear regression technique. The RUL for a given test engine was estimated using a weighted sum of RUL estimates based on the most similar instances [12].