Bearings are one of the most critical components in many industrial machines. Predicting remaining useful life (RUL) of bearings has been an important task for condition-based maintenance of industrial machines. One critical challenge for performing such tasks in the era of the Internet of Things and Industrial 4.0, is to automatically process massive amounts of data and accurately predict the RUL of bearings. This paper addresses the limitations of traditional data-driven prognostics, and presents a new method that integrates a deep belief network and a particle filter for RUL prediction of hybrid ceramic bearings. Real data collected from hybrid ceramic bearing run-to-failure tests were used to test and validate the integrated method. The performance of the integrated method was also compared with deep belief network and particle filter-based approaches. The validation and comparison results showed that RUL prediction performance using the integrated method was promising.

In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

In the age of Internet of Things and Industrial 4.0, the prognostic and health management (PHM) systems are used to collect massive real-time data from mechanical equipment. Mechanical big data has the characteristics of large-volume, diversity and high-velocity. Effectively mining features from such data and accurately identifying the machinery health conditions with new advanced methods become new issues in PHM. A major problem of using the existing PHM methods for machinery fault diagnosis with big data is that the features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise, limiting their capability in fault diagnosis with big data. This paper presents a deep learning based approach for bearing fault diagnosis using acoustic emission (AE) sensors with big data. Different from widely used shallow neural network architecture with only one hidden layer, the branch of machine learning methods with multiple hidden layers are regarded as deep learning method. The presented approach pre-processes AE signals using short time Fourier transform (STFT) and extract features. Based on the simply processed AE features, an optimized deep learning structure, large memory storage retrieval neural network (LAMSTAR) is used to perform bearing fault diagnosis. The unique structure of LAMSTAR enables it to establish more efficient and sparse distributed feature maps than traditional neural networks. By leveraging the labelled information via supervised learning, the trained network is endowed with discriminative ability to classify bearing faults. The AE signals acquired from a bearing test rig are used to validate the presented method. The test results show the accurate classification performance on various fault types under different working conditions, namely input shaft rotating speeds. It also proves to be effective on diagnosing bearing faults with relatively low rotating speeds.

Plastic bearings have a wide range of industrial applications due to their many desirable properties such as lightweight, low friction coefficient, chemical resistance, and ability to operate without lubrication. Timely bearing fault diagnosis can prevent equipment failure and costly downtime. In recent years, developing machine learning based bearing fault diagnosis with few labelled data has attracted a lot of attentions as datasets with fault labels are rare in many industrial applications. One effective approach to meet the challenge is few-shot learning. Among many approaches, utilizing a good pre-trained deep learning model to achieve few-shot learning is an effective and efficient alternative. In this paper, a pre-trained deep learning model called CLIP that combines image processing and natural language processing (NLP) is adopted to few-shot learning for plastic bearing fault diagnosis. We explore the feasibility of leveraging CLIP model in the realm of bearing fault diagnosis via few-shot learning. Specifically, we tackle the challenges posed by CLIP's creation of requisite text prompt embeddings for the diagnosis of mechanical faults, within a few-shot learning framework. Our investigation illuminates the remarkable capability of CLIP to adapt to new tasks with minimal examples, a feature we exploit to devise a solution for plastic bearing fault diagnosis. The effectiveness of the few-shot learning method with CLIP is demonstrated using vibration data collected from plastic bearing seeded fault tests in the laboratory.

Full ceramic bearings are critical components in many full ceramic and oil-free food processing and medical equipment. Developing effective full ceramic fault diagnostic methods is important. Supervised deep learning approaches have been considered promising for fault diagnosis in the era of big data where abundantly labelled datasets are available. However, in many industrial applications, datasets with fault labels are rare. This challenge has motivated the task for developing deep learning approaches for fault diagnosis with few training examples. To meet the challenge, one attractive direction is to use available pre-trained deep learning architectures to do fault diagnosis with only few examples. Specifically, this paper investigates the effectiveness of using pre-trained deep learning architectures successfully used in natural language processing to achieve few-shot learning for full ceramic bearing fault diagnosis using acoustic emission signals.

Autonomous Prognostics and Health Management (Autonomous PHM) refers to the capability of a system to independently monitor, diagnose, predict, and manage its own health status without human intervention. It combines traditional PHM functions with autonomy and intelligent decision-making to enable self-sustaining operation, especially in complex or remote environments. The key characteristics of an autonomous PHM system include: (1) self-monitoring: continuous collection and analysis of sensor data to assess system health in real time; (2) self-diagnosis: identification of faults, anomalies, or degradations using AI, machine learning, or model-based reasoning; (3) self-prognosis: prediction of remaining useful life (RUL) or time to failure based on current and historical data; (4) autonomous decision-making: autonomous selection and execution of maintenance or mitigation actions (e.g., reconfiguration, load reduction); (5) adaptability: adapt pre-trained models (e.g., for fault detection or RUL estimation) from one system or component to another with limited new data; (6) minimal human oversight: designed to function reliably with little to no manual input, particularly useful in inaccessible or high-risk settings (e.g., space missions, underwater robotics, military systems). A few challenges remain for developing an effective autonomous PHM system: (1) learning with limited labeled data: limited availability of failure data for training ML models; (2) cross-platform autonomy: autonomous PHM systems often operate in varied conditions or on different equipment types. PHM functions should be adapted from one system or component to another to reduce the need to retrain models from scratch in every new setting. (3) scalability: autonomous PHM systems should scale to large, complex systems (e.g., fleets of aircraft or satellites). A model trained on one unit can be transferred to other units in the fleet to scale autonomous PHM capabilities efficiently. In this paper, the development of an autonomous PHM system by integrating self-supervised learning and large language models (LLMs) is presented. The effectiveness of the autonomous PHM system is demonstrated with an application to turboshaft engine torque prediction.