In the recent years, industrial equipment and machines are rapidly advancing in the direction of high efficiency, high accuracy, increased number of automated and complex operations, ultimately leading to frequent machine breakdowns, equipment failures and accidents [1] . As a result, intelligent condition monitoring and health identification systems are gaining increasing importance for enhancing the security and reliability of industrial machines and improve the overall quality and performance of manufacturing systems, along with the prevention of costly downtime and equipment failures [2] [3] [4] [5] .
As the most crucial and versatile component in various process industries, pumps are often subjected to different types of failures arising from defective bearings, rotor faults, eccentricity, shaft imbalance, motor defects etc. In order to mitigate the potential risks and reduce the maintenance costs incurred due to pump failures, early fault detection and health monitoring systems for pumps have been intensively developed in recent years. Majority of the research studies have focused on condition monitoring of rotating machines and components such as bearings, gearboxes, rotary pumps, piston pumps, turbines, turbomolecular pumps, screw compressors etc. [6] [7] . Vacuum pumps play a pivotal role in various industrial applications such as integrated circuit semiconductor manufacturing domain, space environment simulator applications, chemical and pharmaceutical industries [8] [9] [10] etc. However, studies related to fault detection and diagnosis of vacuum pumps are very scarce [8] [9] . Therefore, to ensure smooth running of industrial processes, it is a highly necessary task to develop effective health monitoring systems for
In the recent years, industrial equipment and machines are rapidly advancing in the direction of high efficiency, high accuracy, increased number of automated and complex operations, ultimately leading to frequent machine breakdowns, equipment failures and accidents [1] . As a result, intelligent condition monitoring and health identification systems are gaining increasing importance for enhancing the security and reliability of industrial machines and improve the overall quality and performance of manufacturing systems, along with the prevention of costly downtime and equipment failures [2] [3][4] [5] .
As the most crucial and versatile component in various process industries, pumps are often subjected to different types of failures arising from defective bearings, rotor faults, eccentricity, shaft imbalance, motor defects etc. In order to mitigate the potential risks and reduce the maintenance costs incurred due to pump failures, early fault detection and health monitoring systems for pumps have been intensively developed in recent years. Majority of the research studies have focused on condition monitoring of rotating machines and components such as bearings, gearboxes, rotary pumps, piston pumps, turbines, turbomolecular pumps, screw compressors etc. [6] [7] . Vacuum pumps play a pivotal role in various industrial applications such as integrated circuit semiconductor manufacturing domain, space environment simulator applications, chemical and pharmaceutical industries [8] [9] [10] etc. However, studies related to fault detection and diagnosis of vacuum pumps are very scarce [8] [9] . Therefore, to ensure smooth running of industrial processes, it is a highly necessary task to develop effective health monitoring systems for
In the recent years, industrial equipment and machines are rapidly advancing in the direction of high efficiency, high accuracy, increased number of automated and complex operations, ultimately leading to frequent machine breakdowns, equipment failures and accidents [1] . As a result, intelligent condition monitoring and health identification systems are gaining increasing importance for enhancing the security and reliability of industrial machines and improve the overall quality and performance of manufacturing systems, along with the prevention of costly downtime and equipment failures [2] [3][4] [5] .
As the most crucial and versatile component in various process industries, pumps are often subjected to different types of failures arising from defective bearings, rotor faults, eccentricity, shaft imbalance, motor defects etc. In order to mitigate the potential risks and reduce the maintenance costs incurred due to pump failures, early fault detection and health monitoring systems for pumps have been intensively developed in recent years. Majority of the research studies have focused on condition monitoring of rotating machines and components such as bearings, gearboxes, rotary pumps, piston pumps, turbines, turbomolecular pumps, screw compressors etc. [6] [7] . Vacuum pumps play a pivotal role in various industrial applications such as integrated circuit semiconductor manufacturing domain, space environment simulator applications, chemical and pharmaceutical industries [8] [9] [10] etc. However, studies related to fault detection and diagnosis of vacuum pumps are very scarce [8] [9] . Therefore, to ensure smooth running of industrial processes, it is a highly necessary task to develop effective health monitoring systems for 48th SME North American Manufacturing Research Conference, NAMRC 48 (Cancelled due to accurate, fast and reliable health identification of industrial vacuum pumps.
The traditional methods for vibration-based health identification usually divide the raw vibration signal into many samples, out of which, a portion is used as training data and the remainder is used as testing data. In other words, these methods perform the validation of the built model in the same data distribution, and thus they assume that the training and testing data have similar distributions [11] . However, in real world applications, there exists a distribution discrepancy between the training and test data distributions due to varying load conditions, work environments and fault intensities, causing the performance of these methods to drop significantly. The diagnostic model developed using such methods, is highly dependent on the training data, and the generalization error is thus, not sufficient to achieve a good performance on the testing data from a different application domain. This phenomenon, in which the developed model is unable to achieve a strong generalization ability, is referred to as the cross-domain learning problem [12] . The supervised training data used for model development is defined as the source domain and the unsupervised testing data is regarded as the target domain.
In practical situations, collecting sufficient labeled training samples, for validating the model, to make it suitable for application in the target domain, is a very expensive and difficult task. Consequently, there is a need to develop domain adaptation techniques that can resolve this issue of distribution discrepancy between the source and target domains by constructing a model in source domain that can adapt itself for use in a new but related target application domain. Instead of iterating the process of model building and training every time for a new diagnosis, transfer learning can be used as an efficient tool for improving the model performance and enhancing its applicability in any related target domain, by leveraging the feature knowledge learnt from the source domain.
Generally, transfer learning is a promising technique for making the developed models, more flexible and practical in various areas, such as object recognition, image classification, speech recognition, feature learning etc. Domain adaptation is one of the main approaches in transfer learning, which assumes that the label spaces of the source and target domains are identical. This approach has been successfully developed and popularly applied in different health monitoring studies. The concept of domain adaptation is illustrated below in Fig. 1 . Due to domain shift between different scenarios, the model developed from the source-domain data cannot be directly applied on the target domain data. However, since domain adaptation techniques draw all the data into the same region, domain-invariant features can be learnt, and the diagnostic knowledge can be generalized well enough, to apply on the target domain-data.
This paper proposes a deep learning-based health identification method for vacuum pumps. Domain adaptation techniques are used to learn generalized machine health condition knowledge from multiple pumps. The raw vibration data is directly used as model input for health identification. Experiments on a real-world vacuum pump dataset are implemented for validation. The proposed method is able to achieve promising cross-machine health identification performance, where the training and testing data are from different pumps. Therefore, the proposed method offers an effective health identification approach for implementation in the real industries.
The remainder of this paper starts with the proposed methodology in Section 2. The details related to the experimental data set are provided in Section 3. The experimental results and benchmarking with other methods, are presented in Section 4. We close this paper with conclusions in Section 5. 
Convolutional Neural Networks (CNN), which were originally designed for image classification purposes [13] , are now widely used for analyzing complex and fluctuating vibration signals of bearings, induction motors, gearboxes etc. [14] because of their significant characteristics such as shared network weights, locally receptive fields and spatial subsampling (spatial pooling) [15] [16] [17] [18] . Using multiple filters, the convolutional layers of the network perform convolutional operation on the raw input data in order to generate meaningful feature maps. In this study, a 1D-CNN architecture is used for health identification of vacuum pumps. Fig. 2 shows the proposed CNN architecture for this problem. Initially, the raw input data is arranged in a two-dimensional (2D) format, with its dimension in the form of Ns x Ndim, where Ns is the number of samples and Ndim is the sample dimension. Next, there are three stacked 1-D convolutional layers in the network, for feature extraction purpose, that have the same configuration, which involves NF number of filters with size LF. As the number and size of each filter in each layer of the network significantly affects the network performance, the filter number is kept as 50 and the filter length is also selected as 50 in this study.
During convolution, zero-padding operation is used to prevent changes in the dimensions of feature maps. A maxpooling layer is placed after every convolutional layer to effectively reduce the dimensionality without losing significant spatial information. The high-level feature representation obtained at the end of the last convolutional layer is flattened and passed through a fully connected layer with 512 neurons. In order to avoid network overfitting, regularization technique such as dropout with rate of 0.5 is used in this layer. 
At the end, a softmax classifier with three neurons, representing the three classes respectively, is used to predict the health condition class. The leaky ReLU (rectified linear units) [19] is used as the activation function in the network, as it does not undergo gradient diffusion or vanishing during network training process and provides better performance in deep architectures. In each layer, the Xavier kernel initializer is used for initialization of weights and biases [20] and Adam algorithm [21] is adopted for network optimization.
While addressing the problem of domain adaptation, the major challenge is calculating and minimizing the difference between the data distributions of the source and target domains. There are various parametric estimation criteria such as KL divergence and Bregman divergence that are used for estimating the distance between two different distributions. In this study, maximum mean discrepancy (MMD) as a nonparametric estimation of distance between data distributions, is used for measuring the distribution discrepancy [22] [23] . MMD is the squared value of distance between kernel embeddings of marginal probability distributions in the reproducing kernel Hilbert space (RKHS) [24] . As shown in equation (1), it can be mathematically expressed as follows:
where S and T denote the data distributions of source and target domains respectively, and ℋ represents the reproducing kernel Hilbert space. The choice of kernel significantly influences the process of MMD computation as stated in [25] . Hence, in order to utilize the power of multiple kernels and design a principled approach for efficient kernel selection, a mixture of 5 Radial Basis Function (RBF) kernels is used in this study, whose bandwidth parameters are 1, 2, 4, 8 and 16.
In this study, the source and target domains share the same health condition categories and hence, the health identification tasks are the same for both domains. As the training data from source domain used for network tuning is labeled, the first optimization objective involves minimization of classification error for the output obtained from the softmax classifier. For achieving this objective, the cross-entropy loss function ℒc is adopted in this case [26] .
In addition, for effectively eliminating the bias between the source and target domains, and enhancing the classifier's generalization ability, single-layer maximum mean discrepancy (MMD) between the source and target distributions is considered as an optimization objective [15] for drawing the two distributions closer to each other using the feature representations learnt during network training. In this case, MMD loss is calculated between the output of the fully connected layer for source and target samples. The multi-kernel MMD loss function used in this case, can be mathematically represented using equation (2) as:
where, S f and T f , denote the source and target domain representations in the last fully connected layer of the network, k denotes the set of kernel filters used, and MMDk (S f ,T f ) is the MMD loss computed between the learnt feature representation of the two domains. Hence, the overall objective function by combining the cross-entropy loss with the MMD loss mentioned above, is represented using equation (3) as:
where β represents penalty co-efficient whose value is set to 1 in this study for simplicity.
In this study, a vacuum pump health identification dataset, collected from the real industrial scenario, is used to validate the proposed method. Specifically, the machinery condition monitoring data from multiple vacuum pumps are collected by the AU Optronics Corp., and the vacuum pump structure is presented in Fig. 3 . From this dataset, the vibration data from six vacuum pumps, i.e. PEI11, PEI21, PEI23, ICP11, ICP31 and ICP13 pumps, is prepared and considered for analysis. The pumps are at different health conditions during their operation, and the data over a period of two months, is collected for investigation. The vibration acceleration signals in three directions are measured, and the x-direction data is used in this paper. The detailed health information is shown in Table 1 , which is determined based on the pump machine usage, maintenance record and operating performance. In general, three machinery health conditions are considered, i.e. low, medium and high-level degradation. In this study, the transfer learning tasks in health identification are investigated, and the source and target domain data are from different vacuum pumps. Specifically, four health identification transfer tasks are implemented, and their descriptions are shown in Table 2 . The default parameters used in this paper are presented in Table 3 . The sequentially collected vibration acceleration data is split into multiple samples, and each sample contains Ndim data points. Nsource and Ntarget denote the number of the source and target domain samples, respectively. Nsource 650
Ntarget 600
Ndim 500
In order to present fair evaluations of the proposed method, different approaches are implemented for performance comparison. Specifically, the following methods are implemented.
k-NN: The k-nearest neighbor (k-NN) method has been popularly used for unsupervised learning. The labels of the nearest neighbors of the testing sample are adopted for predicting the health condition.
SVM: Support vector machines (SVM) are widely used supervised machine learning methods for regression and classification problems. The structural risk minimization criteria in the statistical learning theory is employed for model establishment.
TCA: Transfer component analysis (TCA) is a popular method for finding a feature subspace in the domain adaptation approach, which assumes the source and target data distributions are similar. For the obtained subspace, an SVM model is developed with the source data, and then the built model is used for testing.
The basic deep convolutional neural network-based method is implemented for comparison, where only the supervised learning on the source domain data is considered, and the proposed domain adaptation scheme is removed.
CNN -With DA: This is the proposed method which implements domain adaptation in the deep learning framework. Fig. 4 shows the experimental results of different methods on the four transfer tasks. It can be observed that the proposed method generally achieves the highest testing accuracies in different scenarios. Close to 100% accuracies are obtained in the cross-domain health identification tasks, using the proposed domain adaptation method. The other compared approaches basically obtain low testing accuracies. The results of the deep learning-based method, i.e. CNN -Without DA, are relatively better, but still not effective in this problem. The other conventional signal processing methods mostly obtain less than 50% testing accuracies, which cannot be applied in this industrial problem.
It should be noted that the health identification problem investigated in this paper is challenging in the data-driven studies, since the training and testing data are from different vacuum pump machines. While the conventional approaches such as k-NN and SVM, have achieved good results in some basic tasks, where the training and testing data are collected from the same machine, they are less effective in such crossmachine health identification problem setting.
The experimental results indicate that the proposed domain adaptation technique in the deep learning framework is well suited for this challenging industrial problem. The model generalization ability can be largely improved, and the established model can be used in different pumps, which increases the applicability of the data-driven model in the real industrial environment. The diagnostic performance of the conventional algorithms such as k-NN and SVM largely depends upon the proper extraction of features and hypermeter tuning. For optimizing the prediction capacity of these methods, multiple experiments for different hyperparameter settings were performed and the results for these tests are summarized in Table 4 and Table 5 . As observed in Table 4 , the most optimum performance of k-NN was achieved when the number of neighbors was 7. Table 5 illustrates that the performance of SVM was optimized when the polynomial kernel of degree 8 was selected for health identification task. However, they are still far less effective in the health identification problems. Next, the learnt features from the raw vibration acceleration data are visualized to show the effectiveness of the proposed method. The popular t-SNE technique is adopted for dimension reduction, and the main focus for visualization is kept on the learnt high-level representations in the last fully connected layer of the network. Fig. 5 presents the visualization results on the transfer learning task T2. It is noted that without the proposed domain adaptation technique, noticeable gaps exist between the data distributions of the same health conditions in different domains. Consequently, the learnt health identification knowledge from the source domain cannot be well applied in the target domain. Using the proposed method, it can be observed that the data of the same classes are projected into the same regions across different domains. In this way, generalized domaininvariant features are obtained, and the learnt knowledge can be well applied on new vacuum pumps. Correspondingly, high testing accuracies are achieved by the proposed method as shown in Fig. 4 . Therefore, the effectiveness of the domain adaptation method is quantitatively validated in this section.
In this paper, a deep learning-based health identification method for vacuum pumps is proposed with domain adaptation technique. The raw vibration acceleration data is directly used as model input, which is easy for applications in the real industries. The maximum mean discrepancy metric is introduced for domain adaptation, the minimization of which, ensures extraction of generalized features that are used for diagnostics. Experiments on a real-world vacuum pump dataset are carried out, and the results suggest that the proposed method offers a promising tool for industrial vacuum pump health identification tasks.
Despite the promising results, the target-domain data is supposed to be available for model training, in the proposed framework. To increase the applicability of this health identification method, further research works will be focused on developing generalized condition monitoring models without target-domain data.