Transfer Learning for Induction Motor Health Monitoring: A Brief Review

Abstract

With advancements in computational resources, artificial intelligence has gained significant attention in motor health monitoring. These sophisticated deep learning algorithms have been widely used for induction motor health monitoring due to their autonomous feature extraction abilities and end-to-end learning capabilities. However, in real-world scenarios, challenges such as limited labeled data and diverse operating conditions have led to the application of transfer learning for motor health monitoring. Transfer learning utilizes pretrained models to address new tasks with limited labeled data. Recent advancements in this domain have significantly improved fault diagnosis, condition monitoring, and the predictive maintenance of induction motors. This study reviews state-of-the-art transfer learning techniques, including domain adaptation, fine-tuning, and feature-based transfer for induction motor health monitoring. The key methodologies are analyzed, highlighting their contributions to improving fault detection, diagnosis, and prognosis in industrial applications. Additionally, emerging trends and future research directions are discussed to guide further advancements in this rapidly evolving field.

1. Introduction

Induction motors (IMs) are driving forces for modern industries owing to their ruggedness and efficient speed control operation. IMs can be of the wound type or squirrel cage type, and their usage depends on the given industrial requirements. IMs are often preferred over other types of motors as they are less expensive, more robust, and capable of reliable operations in challenging ambient conditions. IMs, particularly those of the squirrel cage type, have been the principal workhorse in many industries [1]. In general, IMs are robust machines, but faults are inevitable. These faults can lead to complete motor failure. Therefore, reliable health monitoring of IMs is crucial for avoiding unnecessary downtime [2]. An unexpected failure might lead to a costly standstill in the industry, which should be avoided by detecting faults precisely. IMs consist of many mechanical and electrical components, such as a motor frame, stator windings, a rotor cage, rolling bearings, a fan, a motor shaft, and many others. They are exposed to external conditions such as unstable supply voltage, unstable current sources, overloads, unbalanced loads, and electrical stresses, which can lead to faults in IMs. These faults can include electrical and mechanical faults. The mechanical faults include bearing faults, rotor faults, and eccentricity, whereas the electrical faults include stator winding faults and rotor winding faults. The conventional techniques used for fault detection include vibration monitoring, current monitoring, temperature monitoring, and so on. With rapid advancements in artificial intelligence (AI), machine learning (ML) and deep learning (DL) have also gained substantial attention in the last decade. ML algorithms such as the support vector machine (SVM), decision trees (DTs), k-Nearest Neighbor (kNN), and many more have been abundantly used for the health monitoring of IMs. ML models require feature engineering, which includes feature extraction and selection. Feature engineering is vital for the efficient performance of ML models. Effective ML models require large, high-quality, and labeled datasets representing both healthy and faulty conditions. Other authors [3] have used an SVM-based approach for bearing fault detection in IMs. SVM was utilized along with the continuous wavelet transform (CWT) on vibration signals during the startup. Another study [4] presented a fault analysis of IMs using the frequency spectrum determination and SVM. The features were evaluated using the fast Fourier transform and the final diagnosis was made using SVM. The authors of [5] proposed a bearing fault detection using the SVM classifier in combination with principal component analysis (PCA). The feature extraction involved selecting the four highest peaks in the frequency spectrum and PCA. Finally, SVM was deployed to identify the fault severity level. The authors of [6] used the ensemble and decision tree (DT) classifier for the detection of rotor and bearing faults in IMs using current data for the analysis. Another study [7] proposed a fault diagnosis (FDG) mechanism of IMs using the combination of DTs and an adaptive neuro-fuzzy inference system (ANFIS). The authors of [8] proposed a bearing FDG, using the stator current for statistical features estimation and DT, random forest (RF), and kNN as classifiers. The authors of [9] proposed a method for the condition monitoring of bearings using DTs and extreme MLs. A DT algorithm was deployed to extract features from vibration signals and features were fed to the extreme ML for IM health monitoring. The authors of [10] developed a bearing fault detection strategy using the supply currents with decision trees. Another study [11] proposed a motor faults detection strategy using the features extracted from sound signals with the discrete wavelet transform and local binary pattern methods in combination with neighborhood component analysis; classifiers including SVM and kNN were deployed for decision making. The authors of [12] proposed a different IM fault method using the feature vector extracted from the short-term Fourier transform (STFT) from the sound and acceleration signals and random forest (RF) classifier. Another study [13] proposed an IM fault detection mechanism using ML algorithms such as RF, artificial neural network (ANN), DTs, RF, and kNN models. Among these models, the RF algorithm obtained the highest accuracy. In [14], a comparative study was conducted using the ML and DL methods, where the kNN-based approach reached an accuracy of 90.55%, the SVM-based approach had an accuracy of more than 90%, the DT-based method had an accuracy of 87.76%, and RF in combination with fast Fourier transfer achieved an accuracy of more than 95%.

However, despite the advantages of ML, DL has also gained substantial attention due to its ability to handle complex patterns and large datasets, as well as its autonomous feature extraction, superior performance in complex scenarios, and adaptability to diverse operating environments. DL algorithms inherently offer numerous advantages but also require substantial amounts of data for efficient performance. In order to address these issues, transfer learning (TL) offers an effective solution. TL allows the usage of knowledge gained from one task (or dataset) to be applied to another related task, even when labeled fault data for the specific condition are limited. This proves to be valuable in real industrial settings where acquiring large datasets is challenging and impractical. In the context of IM health monitoring, where defect data are frequently limited, unbalanced, or costly to acquire, traditional deep learning models have considerable difficulty in their ability to generalize, as they require huge amounts of labeled data. TL, on the other hand, lessens the need for large labeled datasets in the target domain by utilizing information from previously trained models on similar tasks or domains. High-level feature representations that have been learned from an abundance of source data, such as vibration signals from comparable rotating machinery or synthetic datasets, can be tailored to the particular purpose of motor defect detection thanks to TL. In industrial settings where defect occurrences are uncommon and annotated data is scarce, this capacity is especially beneficial. TL is a strategically advantageous approach over traditional DL methods in motor health diagnostics because it reduces the need for large-scale labeled data and shortens the time needed for model training. This enhances model generalization and applicability, particularly in the face of domain shifts and changing operational conditions. TL also offers improved generalization across domains, which helps model to adapt to new operating conditions, different motor types, or varied environments by leveraging the pretrained models from similar problems [14,15]. This is especially valuable for FDG under variable working conditions or when transferring diagnostic capabilities between different machines. Also, by using pretrained neural networks, TL reduces the burden of training the model from the scratch [16]. TL can help in achieving effective performance even under the limited training samples, which makes them suitable for early fault detection in real-world scenarios. In a nutshell, TL accelerates and improves the fault detection by making models more adaptable, data-efficient, and robust to changes in operating conditions. Considering the numerous advantages of the TL, this paper aims to briefly review the advancements in the application TL in IM health monitoring. The paper is divided into multiple sections. Section 2 describes the fundamentals of TL. Section 3 provides the overview of the motor health monitoring and Section 4 entails the TL-based approach for the motor health monitoring. Section 5 presents the emerging trends in motor health monitoring along with future possibilities and Section 6 concludes the review.

2. Fundamentals of Transfer Learning

Transfer learning (TL) is an ML approach in which a model developed for a source task is reused as a starting point for a model on a target task. It improves learning in the target domain by using the knowledge from the source domain. It can be helpful in practical scenarios such as motor health monitoring where obtaining sufficient labeled data is challenging. For a given domain defined as $D=\left\{x,P\left(x\right)\right\}$, $x$ denotes the feature space and $P\left(x\right)$ denotes the marginal probability distribution of the data. A task is defined as $T=\left\{y,f(.)\right\}$, where $y$ denotes the label space and $f:x\to y$ is the predictive function. In TL, we have a given source domain $D_s=\{x_s,P_s\left(x\right)\}$ with a source task $T_s=\{y_s,f_s\left(.\right)\}$ and a target domain $D_T=\{x_T,P_T\left(x\right)\}$ with a target task $T_T=\{y_T,f_T\left(.\right)\}$. The goal is to improve the learning of $f_T\left(.\right)$ using knowledge from $D_s$ and $T_s$, particularly when $D_s\ne D_T$ or $T_s\ne T_T$. There can be several types of TL, including inductive TL, transductive TL, and unsupervised TL. Inductive TL is characterized by $T_s\ne T_T$, where some labeled data exists in the target domain. Transductive TL involves $D_s\ne D_T$ with $T_s=T_T$, and unsupervised TL involves the situation where neither source nor target domains have labeled data. It often focusses on clustering or representation learning. Figure 1 shows the basic layout of the TL process, where the source model is trained on source data for a source task and knowledge from this model is then transferred to the target model, which is adapted using target data to perform a new but related task more efficiently. Figure 2 shows the different core areas of TL.

3. Overview of Motor Health Monitoring

Motor health monitoring has gained significant attention for its wide usage across a range of industries and sectors. It is vital to monitor the health of the motor to ensure the reliability, safety, and efficiency of industrial operations. IMs are widely used for various applications due to their robustness and simplicity. The primary objective of health monitoring is to detect and diagnose faults early, thereby minimizing downtime, reducing maintenance costs, and preventing catastrophic failures [19]. The health monitoring techniques for IMs are based on a variety of signals acquired from the IMs like current, vibration, temperature, and many others. With advancements in computational resources, AI has been integrated with these signals along with signal processing techniques for efficient health monitoring of IMs. The modern health monitoring techniques not only detect faults but also predict the remaining useful life (RUL) using the prognostics and health management (PHM) frameworks. These systems integrate real-time monitoring, predictive models, and decision-support tools to enable predictive maintenance and optimize asset management. A generic layout of the FDG method using a DL model is shown in Figure 3.

Authors [21] developed an FDG mechanism for single-phase IMs using the infrared thermography. Both conventional ML and DL have been utilized for the IMs health assessment and achieved a classification accuracy of more than 98%. Authors [22] proposed fault detection and diagnosis methods of motors using a 1D convolutional neural network (CNN) with multi-channel vibration signals. Six different types of faults were detected using the two accelerometers measuring in two different directions. Authors [23] proposed a DL approach based on CNN for the classification and diagnosis of faults using the vibration signals. Authors [24] proposed a hybrid DL model based on the CNN and deep forest for bearing fault detection by converting the vibration signals into time-frequency images using the continuous wavelet transformation. Authors [25] proposed an accurate fault detection mechanism for the bearings of the motor using the cyclic spectral coherence and CNN. Also, group normalization is employed in CNN for normalizing the feature maps of the network, which helps in reducing the internal covariant shift induced by data distribution discrepancy. Authors [26] proposed an effective FDG mechanism for IMs based on the wavelet and convolutional attention neural network. The analysis showed that this approach achieved an accuracy of 99.43%. Authors [27] proposed an IM FDG mechanism with the help of a multi-input CNN model. The features of the vibration and acoustic signals were fused and used for the IM’s FDG via multi-input a CNN model. Authors [28] proposed a CNN-based approach for the incipient stator FDG of the inverter-fed IM. Experimental tests were conducted on a specially designed setup with 3 kW IM, which was designed to emulate the inter-turn short-circuits in each of the three phases of the machine. Authors [29] proposed an early fault detection technique in IMs with the help of a deep CNN–long short-term memory (LSTM) model and CNN-gated recurrent unit (GRU) model. Authors [30] proposed a Shrinkage Mamba Relation Network combined with out-of-distribution data augmentation for fault detection and localization in rotating machinery under zero-faulty data conditions. Authors [31] developed a fault detection mechanism for IMs using the DL model and vibration signals. Authors [32] developed an intelligent fault detection mechanism for bearing faults by using an automatic feature learning neural network that uses the raw vibration signals as input and uses two CNNs with varying kernel size for feature extraction and LSTM model for identifying the fault type based on these features. It achieved an accuracy of more than 98.46%. Authors [33] proposed the CNN-based multi-signals FDG of IMs using the single- and multi-channel datasets.

TL has proven to be a very successful method in the field of motor health monitoring when compared to training DL models from scratch [34,35,36,37]. This is mainly because of the inherent difficulties with generalization, processing resources, and data availability. It is frequently challenging and costly to obtain extensive labeled datasets for RUL prediction, degradation investigation, or fault detection. Because motor defects can happen infrequently or under certain operating conditions, it is not feasible to gather the wide range of data required to properly train deep neural networks (DNNs).

4. Transfer Learning in Motor Health Monitoring

In real-world motor health monitoring scenarios, traditional DL techniques often require large volumes of labeled data to attain good accuracy and generalization. This problem is addressed by TL, which makes it possible to apply information acquired from large-scale, frequently unrelated source domains where there is an abundance of labeled data to target domains with lack of data. Rich feature representations, such as edge detectors, texture analyzers, or temporal patterns, have already been captured in the early layers of pretrained models, particularly those trained on large datasets like ImageNet or large time-series repositories. These representations are frequently generic and transferable across different domains. These pretrained models can be adjusted on the particular motor fault data when used for motor health monitoring. This enables the network to retain the strong lower-level features learned from the source task while adapting higher-level feature representations to the unique characteristics of motor vibrations, acoustic emissions, or current signals. Because the model begins with a well-informed initialization rather than random weights, this not only lowers the amount of data needed but also greatly speeds up the model’s convergence. Additionally, TL improves the model’s capacity for generalization, which is crucial in industrial settings where motors may run under a range of loads, speeds, and climatic circumstances. TL enables the model to capture more broadly applicable properties that are resilient under a variety of operating conditions, as opposed to overfitting to a limited dataset. TL is particularly beneficial from a computational perspective since it eliminates the need for extensive training, which requires a substantial investment in time, money, and computing power. The technique is more resource-efficient and practical for industries with limited computing infrastructure because the pretrained weights provide a foundation that just needs to be fine-tuned or partially retrained. Furthermore, TL enables ongoing learning and adaptability as new kinds of motor faults or operating conditions appear; models can be gradually updated with new data while maintaining previously learned information, which is essential for creating reliable and scalable PHM systems. In a nutshell, TL is a very appealing substitute for traditional DL techniques in the fields of intelligent maintenance and industrial diagnostics since it provides a practical, effective, and efficient means of advancing motor health monitoring by overcoming the constraints of data scarcity, improving model generalization, speeding up training, and lowering computational costs.

Authors [34] proposed an FDG mechanism using the TL. The sensor data were converted to images using the wavelet transformation for obtaining the time–frequency distribution. In [17], a deep TL method was developed for fault detection in the IMs using the TL via pretrained ResNetV2 model. This approach achieved an accuracy of more than 99%. Authors [38] proposed a fault detection mechanism for IMs using the time domain and spectral imaging-based TL with the help of vibration data. The short-time Fourier transform (STFT)-based spectrograms were developed for the analysis and achieved an accuracy of 97.67%. The layout of this approach is given in Figure 3. TL is demonstrated with a pretrained CNN in Figure 4. A new CNN model for spectrogram-based classification is trained using parameters that were learned from an image classification challenge. By utilizing knowledge from the prior image-based model and facilitating effective feature reuse, shared convolutional layers minimize training effort for the new task. Authors [39] proposed a deep TL method based on sparse auto-encoder (AE) for bearing FDG. This method uses a three-layer sparse AE to extract features from raw data and applies a maximum mean discrepancy term for minimizing the discrepancy penalty between training and test data features. Authors [40] proposed a TL with CNNs for IMs FDG based on the small amount of the target data. Results have demonstrated that this method achieved reasonable diagnostic accuracy with inadequate target data. Authors [41] proposed a deep adversarial TL model for identifying the emerging faults. One-dimensional CNN is used to learn invariant features from the vibration signals of the source and target domains. A final decision boundary is developed for identifying emerging faults by training a classifier to recognize some target samples as new ones.

Authors [16] developed a TL-based method for FDG using the VGG-19 CNN framework. Thermal images with different IMs conditions were used for the analysis and achieved an overall classification accuracy of 99.8%. Authors [42] proposed a deep TL model for broken rotor FDG for IMs using the time–frequency images and pretrained ResNet18 model. Authors [43] proposed a deep CNN using TL for FDG on the public-domain dataset. One-dimensional signals were converted to gray-scale images, and these were used for the FDG. Authors [44] proposed a TL-based feature transfer method for FDG. A transfer component-analysis-based method was developed to transfer data features between the working conditions. Authors [45] proposed a deep convolutional TL network for intelligent FDG of machines with unlabeled data. This method comprises two modules, namely, condition recognition and domain adaptation. Authors [46] proposed a CNN with TL for intelligent bearing FDG of the motors. The vibration signals were transformed into the spectrogram images by the non-uniform fast Fourier transform with Hamming window for the analysis. These spectrograms were cut into several consecutive sub-spectrograms into 227 × 227 × 3 pixels for the TL-based analysis. Authors [47] developed a motor FDG approach based on modified InceptionV3 model for efficient FDG. The layout and methodology of this approach is given in Figure 5 and Figure 6. Figure 5 illustrates a hybrid model that combines machine learning and deep learning. An InceptionV3-SE model that has been trained is used to extract features from training and test data. Using deep feature representation and SVM’s accuracy, performance is improved as an SVM classifier is trained using these features before performing the final classification on test data. A framework for diagnosing motor faults using thermal imaging is shown in Figure 6. Images are subject to dataset splitting after CLAHE (Contrast Limited Adaptive Histogram Equalization) preparation. The proposed strategy integrates an SVM classifier with SE-enhanced InceptionV3. Precision, recall, F1-score, and accuracy are used to assess the model’s performance, illustrating a successful hybrid technique that combines deep learning and machine learning. This approach was tested on the 369 thermal images of an electric motor with various types of faults.

Authors [48] proposed a TL-based strategy for the bearing FDG under different working conditions using the convolution Wasserstein adversarial networks. Authors [35] proposed a TL-based technique for bearing FDG using scalogram-based images from vibration signals and pretrained VGG19 model. Authors [49] proposed an adaptive deep TL strategy for the bearing FDG using the long short-term memory recurrent neural network model based on instance-TL and joint distribution adaptation via feature TL method. Authors [50] proposed a TL-based deep CNN model for the bearing and broken rotors fault detection (FDT) in IMs. This approach provides the autonomous feature learning capabilities and fault decision-making with minimum human intervention. Authors [51] proposed a technique for bearing and rotor FDG of IMs using the continuous wavelet transformation and deep neural network in combination with TL. Authors [52] proposed acoustic spectral imaging data for the reliable bearing FDG using the TL-based technique in combination with the CNN model. Authors [53] used a passive thermography image for bearing FDT with the help of CNN with TL under changing working conditions. It achieved accuracy in the range of 89–95.4% for the IM dataset. Authors [54] proposed a TL-based improved stacked AE for the bearing FDG. To prevent the vanishing gradient problem and improve feature extraction capability, a convolutional shortcut has been used instead of the sparse term Kullback–Leibler (KL) divergence in the stacked AE. Authors [55] proposed an interpretable domain adaptation transformer based on TL for bearing FDG. A multi-layer domain adaptation transformer was developed for capturing the global information and ensemble attention weight was applied to make the TL model interpretable. Authors [56] proposed a deep adversarial TL method for FDG in rotating machinery. A subdomain adaptation and adversarial learning were introduced for aligning local feature distribution and global feature distribution separately. Authors [57] proposed a generative adversarial network and TL-based FDT approach for rotating machinery under the imbalanced data conditions. Authors [58] proposed an improved TL method for FDT using the adaptive dimension convert CNN and layered alternately TL approach. Authors [59] proposed a residual network based on a deep transfer diagnosis model for bearing faults. Wavelet packets transform and multi-kernel maximum mean discrepancy are combined for deep feature extraction, which helps in achieving better cross-domain invariance and fault state differentiation capability. Authors [60] proposed an unsupervised deep TL method in conjunction with isolation forest for machine FDG. This method has yielded a high accuracy and generality. Authors [61] proposed the Inception-ResNet-v2-based pretrained model for TL for the bearing FDG in the electric motors. The architecture of this approach is given in Figure 7. A table (

Table 1. TL-based approach for IM’s FDT.

Fault Type (Ref.)	TL Approach	Data Type	Details	Model
Bearing fault [16]	CNN-based TL	Thermal images	Utilized pretrained VGG-19 on ImageNet for FDT using thermal images captured by FLIR camera.	VGG19-CNN
Bearing faults [62]	Signal to images conversion with Inception	Vibration signals	Converted vibration signals to 2D time–frequency images	ResNet-34
Multi-faults [63]	Feature extraction with EfficientNetV2	Multi-sensor signals	Converted multi-sensor signals to 2D time-frequency images	EfficientNetV2
Bearing faults [64]	Unsupervised TL	Vibration signal	Employed Squeeze-and-Excitation (SE) attention and Joint Adaptation Network (JAN) for unsupervised diagnosis.	SE + JAN
Bearing faults [65]	TL with ResNet-50	Vibration signal	Fine-tuned ResNet-50 for vibration-signal-based scalogram images	ResNet-50
Bearing and rotor faults [14]	TL with VGG19	Current signals	Feature extraction using VGG19	VGG19
Bearing faults [66]	TL for domain adaptation	Vibration signals	Deep TL-based CNN model for domain adaptation	TL-CNN model
Bearing faults [67]	Joint distribution adaptive (JDA) method	Vibration signals	JDA with deep belief network (DBN)	JDA-DBN
Bearing faults [68]	ResNet-50 for feature extraction	Vibration signals	ResNet-50 for low level feature extraction and multiple scale feature learner to analyze these features	ResNet-50
Rotor damages [42]	ResNet18 model with time–frequency images	Vibration and current signals	CNN with TL in combination with spectrogram images	ResNet18
Bearing faults [57]	Efficient-Net for feature extraction	Vibration signals	TL method based on the time-generative adversarial network	Efficient-Net model
Rotor cage bars and stator windings [69]	CNN for feature extraction	Axial flux	Signal processing by CNN in combination with TL	TL model
Bearing faults [70]	Multichannel feature extraction module	Vibration signals	Multi-order statistics matching sparse wavelet CNN method	TL model
Stator winding faults [71]	TL integrated with digital twin	Motor signals	Time-aware Convolutional Transformer (TaCT) model	TL model
Bearing faults [72]	Unsupervised TL	Vibration signals	Optimized CNN with fast batch nuclear-norm maximization	Unsupervised TL

) has been developed to understand the TL approach along with type of fault/faults detected with type of input and associated models.

5. Emerging Trends and Future Directions

5.1. Emerging Trends

Transfer learning has emerged to play a pivotal role in motor health monitoring. It can help in training the DL model without training the model from scratch. TL leverages the knowledge from pretrained models to improve FDG and predictive maintenance in industrial settings. Pretrained DL models, such as VGG-19, are increasingly applied to IM FDG. Also, capsule networks are gaining attention compared to traditional CNNs, preserving spatial relationships in data through vector-output capsules, which could enhance FDT accuracy in complex motor conditions. IoT (Internet of things)-based integrating sensors with TL models help in enabling continuous, real-time health monitoring of IMs. These systems use edge computing and cloud infrastructure to process large-scale data, improving scalability and reducing latency. Data-driven automation models, including DL and reinforcement learning, are being adopted to analyze dynamic IoT network data, identifying hidden trends and patterns for predictive maintenance. Recent advancements involve TL with DL techniques, such as graph neural networks (GNNs), which model vibration signals as graphs to capture complex relationships, outperforming traditional recurrent neural networks (RNNs) in bearing FDG. Time–frequency analysis methods like wavelet transforms are increasingly used to overcome limitations of Fourier transforms, enabling better detection of time-varying fault signatures in noisy environments. Combining multiple data sources via data fusion techniques enhances FDG robustness. TL addresses challenges of limited labeled data in industrial settings by transferring knowledge from large, general datasets to specific motor health tasks. Techniques like instance reweighting and feature representation transfer are used to adapt pretrained models to target domains. In fault datasets, class imbalance is a recurring problem. To counteract this, TL techniques are being developed in conjunction with data augmentation and synthetic data generation, guaranteeing strong model performance.

5.2. Future Directions

Future research will focus on fine-tuning pretrained TL models to enhance adaptability to specific motor types and operating conditions. This includes optimizing network architectures for faster detection and lower computational costs. Also, combining hybrid TL models with reinforcement learning could enable adaptive FDT in dynamic industrial environments. Implementing TL models on edge devices equipped with application-specific integrated circuits (ASICs) or low-power microcontrollers will lower power consumption and latency, enabling real-time monitoring in environments with limited resources. For large-scale motor monitoring systems, research into dividing computation among cloud, edge, and IoT devices will maximize resource use and enhance scalability. The “black box” aspect of DL in crucial applications like motor health monitoring will be addressed by integrating XAI (Explainable artificial intelligence) with TL models, which will improve interpretability. This is essential for maintaining adherence to safety regulations and fostering trust in industrial environments. The creation of responsible TL systems for industrial usage may be guided by ethical AI models that draw inspiration from mobility and healthcare applications. The current constraint of small-scale or simulated datasets for induction motor defects will be addressed by creating standardized, large-scale datasets, especially for inverter-driven motors. Training and evaluating TL models will become more robust as a result. Research and implementation of TL-based motor monitoring systems could be accelerated by cooperative efforts to establish open-access repositories akin to those in the healthcare industry. Establishing benchmarking procedures should be the goal of future research in order to facilitate equitable comparisons. Furthermore, additional real-world validations are required to evaluate the resilience and practicality of these approaches in industrial contexts. Also, future research can include integration of the advanced AI paradigms such as federated learning and quantum ML with TL of motor health monitoring. To ensure privacy and security in real-world deployments, federated learning makes it possible for collaborative model training across decentralized industrial systems without exchanging sensitive operational data. The potential for quantum ML to improve model optimization in challenging diagnostic tasks and speed up high-dimensional calculations is still in its early stages. By examining these technologies in conjunction with TL, new avenues for computationally efficient, secure, and scalable failure detection and diagnosis may become available. Large-scale, multi-site motor monitoring systems and settings with stringent data governance regulations may benefit greatly from such integration. These methods will supplement present efforts to improve explainability using XAI techniques, hybridize with reinforcement learning, and optimize TL models for edge deployment. It will also address the limitation of data through the production of large-scale, standardized datasets.

6. Conclusions

This paper presents a brief review of the transfer learning application in induction motor health monitoring. It has emerged as a powerful tool for enhancing the performance and generalization of DL models in IM health monitoring, especially in scenarios with limited labeled fault data and varying operating conditions. Reusing knowledge learnt from the other related domains, TL significantly reduces training time, improves model robustness, and accelerates deployment in real-world industrial settings. This brief review covers the application of TL strategies ranging from fixed feature extraction and fine-tuning to domain adaptation. Despite numerous advantages, challenges remain, such as addressing negative transfer, handling high domain variability, and ensuring interpretability and real-time adaptability. In a nutshell, TL has proven to be a cornerstone in the evolution of intelligent, scalable, and data-efficient induction motor health monitoring systems. It promises to play a crucial role in truly autonomous and resilient predictive maintenance frameworks.