Automating Predictive Maintenance Using State-Based Transfer Learning and Ensemble Methods

The act of leveraging sensor data, such as accelerometers on connected industrial equipment, to diagnose and predict failures in roller element bearings is of growing interest in industrial and commercial applications. To do this, a large effort must be made to gather and process data, then apply and manage algorithms to make predictions and derive value. For these reasons, knowledge from previous models and tools to help in the evaluation and adaptation of new data sources using methods such as transfer learning and meta-learning have received significant attention in recent years. This paper aims to provide a methodology for combining such tools and presents a framework for an automated machine learning (AutoML) process by using a combination of pre-existing AutoML tools and new transfer learning methodologies. The framework uses recent observations in vibration analysis and deep learning architectures to abstract away many of the small, but intricate, decisions required for data preparation and model building. This allows for a unique data science strategy and dramatic reduction in the development time required to achieve competitive baseline models when compared to industry averages, while simultaneously improving the ability to address unseen environments. First, a signal processing engine is used to standardize the data, then fingerprinting and unsupervised learning is used to separate data into groups based on a machine's operating state. Meta-learning is then used to evaluate these states as tasks to be associated with a model architecture from a selection of two pretrained models for transfer learning, as well as one custom built model, based on the tasks themselves. In parallel, baseline models are generated using an existing AutoML library. Finally, the top performers from the existing AutoML models are combined with the transferred or built models in an ensemble method regime that uses a Support Vector Machine (SVM) to yield a final classification output. The framework is tested by performing intelligent fault detection on vibration data from roller element bearings of three separate publicly available datasets. The framework attained prediction results consistent with other studies in the field, while dramatically reducing the net amount of user interaction time and number of required user decisions. The system also presents the opportunity for self-serve data and model management, helping data scientists spend more time on the creation of new and unique machine learning applications.