The success of intelligent fault diagnosis of machines relies on the following two conditions: 1) labeled data with fault information are available; and 2) the training and testing data are drawn from the same probability distribution. However, for some machines, it is difficult to obtain massive labeled data. Moreover, even though labeled data can be obtained from some machines, the intelligent fault diagnosis method trained with such labeled data possibly fails in classifying unlabeled data acquired from the other machines due to data distribution discrepancy. These problems limit the successful applications of intelligent fault diagnosis of machines with unlabeled data. As a potential tool, transfer learning adapts a model trained in a source domain to its application in a target domain. Based on the transfer learning, we propose a new intelligent method named deep convolutional transfer learning network (DCTLN). A DCTLN consists of two modules: condition recognition and domain adaptation. The condition recognition module is constructed by a one-dimensional (1-D) convolutional neural network (CNN) to automatically learn features and recognize health conditions of machines. The domain adaptation module facilitates the 1-D CNN to learn domain-invariant features by maximizing domain recognition errors and minimizing the probability distribution distance. The effectiveness of the proposed method is verified using six transfer fault diagnosis experiments.

Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Frechet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN). TFR can reveal nonstationary property of a bearing degradation signal effectively. After acquiring time-series degradation signals, we get TFRs, which contain plenty of useful information using wavelet transform. Owing to high dimensionality, the size of these TFRs is reduced by bilinear interpolation, which are further regarded as inputs for deep learning models. Here, we introduce an MSCNN model structure, which keeps the global and local information synchronously compared to a traditional convolutional neural network (CNN). The salient features, which contribute for RUL estimation, can be learned automatically by MSCNN. The effectiveness of the presented method is validated by the experiment data. Compared to traditional data-driven and different CNN-based feature extraction methods, the proposed method shows enhanced performance in the prediction accuracy.

Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network

Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction

Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-based prognostic methods still lack an effective model that can utilize operational conditions data for remaining useful life (RUL) prediction. This paper develops a novel prognostic method based on bidirectional long short-term memory (BLSTM) networks. The method can integrate multiple sensors data with operational conditions data for RUL prediction of engineered systems. The proposed architecture based on BLSTM networks includes three main parts: first, one BLSTM network is used to directly extract features hidden in the multiple raw sensors signals; second, another BLSTM network is employed to learn higher features from operational conditions signals and the learned features from the sensors signals; and, third, fully connected layers and a linear regression layer are stacked to generate the target output of the RUL prediction. Unlike other AI-based prognostic methods, the developed method can simultaneously model both sensors data and operational conditions data in a consolidated framework. The proposed approach is demonstrated through a case study on aircraft turbofan engines, and comparisons with other popular state-of-the-art methods are also presented.

A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Machinery health indicator construction based on convolutional neural networks considering trend burr

Remaining useful life (RUL) prediction has attracted more and more attention in recent years because of its significance in predictive maintenance. The degradation processes of systems from the same population are generally different from one another due to their various operational conditions and health states. This behavior is defined as unit-to-unit variability (UtUV), which brings difficulty to RUL prediction. To handle this problem, this paper develops a Wiener-process-model (WPM)-based method for RUL prediction with the consideration of the UtUV. In this method, an age- and state-dependent WPM is specially designed to describe the various degradation processes of different units. A unit maximum likelihood estimation (UMLE) algorithm is proposed to estimate the UtUV parameter according to the measurements of training units, without any restriction to the distribution pattern of the parameter. The UtUV parameter is further updated via particle filtering (PF) according to the measurements of the testing unit. In the particle updating process, a fuzzy resampling algorithm is developed to handle the sample impoverishment problem of PF. With the updated parameter, the RUL is predicted through a degradation process simulation algorithm. The effectiveness of the proposed method is verified through a simulation study and a turbofan engine degradation dataset.

A Wiener-Process-Model-Based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

https://www.mdpi.com/1996-1944/10/6/675/pdf

Ningbo Li

Papers

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Machinery health indicator construction based on convolutional neural networks considering trend burr

A Wiener-Process-Model-Based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions