1. Introduction
The deployment of artificial intelligence (AI) is critical for success in the complex industrial sector. In particular, AI solutions have become increasingly important as they assist in developing effective smart services, optimizing production process, and forecasting machinery failure [
1]. Using this information, industrial professionals could make more informed decisions with improved productivity, efficiency, and safety. Consequently, industries are automated, and people have become increasingly linked, more than ever before. Because of the extensive benefits of smart industry, several fields have started to use it. Fields such as agriculture, energy, automobiles, oil, gas, and so on are some of the typical examples. However, advances in the technology of smart industrial applications have become critical for meeting the requirements of industry 4.0 [
2]. The possibilities of using AI in smart industry are relatively diverse and broad. According to the application requirement, the usage of AI provides trusted recommendations, assists with anticipated needs, and manages tasks. Adapting AI-driven technology will provide a competitive benefit through several smart industrial applications [
3]. This is due to the fact that irrespective of industry type and size, AI provides potential solutions to all sectors.
Rotating machinery has become a crucial equipment in industries [
4]. Over recent years, efficient RM has been deployed in the production of accurate machine tool spindles, the latest supersonic vector aircraft engine, efficient marine propulsion motors, massive generator sets, among others—all of which are designed to achieve unmanned, automated, and maximal speeds. For approving the scalability and security of RMs, it is necessary to design smart and proficient health monitoring and FD systems. Incipient fault diagnosis provides a minimum of consequences for the consistency of rotating machines, while it is very easy and simple to handle. However, the features of incipient fault are not highly reliable, and predicting the micro-fault is more difficult than a typical fault [
5].
Several fault diagnoses models were introduced, and they are categorized into three classes: the data-driven method, quantitative model-based method, and qualitative model-based method [
6]. As the difficulty of the current process increases, it becomes increasingly difficult to construct mathematical models that efficiently capture system dynamic behavior [
7]. Consequently, the data-driven method, which only relies on the data derived from the process, is receiving considerable interest. The primary stage in the data-driven method is feature extraction, in which the processed information is converted into a lower dimension, with more informative data. The artificial neural network (ANN)-based method is an alternative way that has gained considerable attention over the last few years [
8]. An artificial neural network is a network of neurons that learn complicated functions over a sequence of non-linear conversions, and, with the emergence of deep learning (DL) methods, it is effectively employed for complicated classification tasks, including speech recognition and image recognition. However, many of the studies used shallow neural networks or neural networks with hierarchical structures. Therefore, the wider possibility of deep neural networks being used to address fault diagnoses has yet to be explored [
9,
10].
This study introduces a novel sandpiper optimization with an artificial intelligence-enabled fault diagnosis (SPOAI-FD) model for intelligent industrial applications. The proposed SPOAI-FD technique involves the design of a continuous wavelet transform (CWT)-based pre-processing approach, which converts the raw vibration signal into a useful format. Moreover, a bidirectional long short-term memory (BLSTM) model is applied as a classifier, and the Faster SqueezeNet model is employed as a feature extractor. For effectively adjusting the hyperparameter values of the BLSTM, the sandpiper optimization algorithm (SPOA) can be applied. In order to highlight the better performance of the presented model, a comprehensive investigation was conducted, comparing the results against benchmark datasets. The major contributions of the study are as follows.
An intelligent SPOAI-FD technique comprising pre-processing, Faster SqueezeNet feature extraction, BLSTM classification, and SPOA-based parameter tuning for fault diagnosis is presented. To the best of our knowledge, the SPOAI-FD technique has never been presented in the literature.
Employ the Faster SqueezeNet model for feature extraction and the BLSTM model for classification.
Hyperparameter optimization of the BLSTM model using SPOA algorithm using cross-validation helps to boost the predictive outcome of the proposed model for unseen data.
2. Literature Review
Wu et al. [
11] designed a CNN for direct learning of the features in the novel vibration signal and Fault Diagnosis (FD). In the study conducted earlier [
12], an ensemble transfer CNN, determined by multi-channel signals, was presented. Primarily, a sequence of the source CNN was changed with stochastic pooling, whereas the Leaky ReLU (LReLU) was pre-trained to utilize the multichannel signals. Secondarily, the learned parameter data of all the individuals’ source CNN was transmitted to initialize the equivalent target CNN after fine-tuning with some of the target-trained instances. At last, a novel decision fusion approach was planned for flexible fusion of all the individuals’ target CNN to obtain the detailed outcome.
In the literature [
13], a new FD approach was proposed based on Max-Relevance Min-Redundancy (mRMR) and Improved Multiscale Dispersion Entropy (IMDE). The mRMR technique was employed for automatic selection of the sensitive features from the candidate multi-scale features without any prior data. At last, the sensitive feature vector was set after which the normalized treatment was recorded. The ELM technique was used to train the intelligent analysis method which produced FD outcomes. In the study conducted earlier [
14], an FD technique was presented based on DCNN and SVM techniques. Being a data-driven DL approach, the DCNN technique was executed in this study to extract the fault feature automatically. The fault-feature data was removed adaptively based on the minute variances from the local fault signal.
Chen et al. [
15] examined a data-driven intelligent FD technique for RM based on a novel Continuous Wavelet Transform-Local Binary CNN (CWT-LBCNN) technique. The presented technique created an end-to-end analysis process without any need for manual extraction of the features. Using the feed and the input vibration signals, the features were taken adaptably, and fault states of the RM were analyzed automatically. Dibaj et al. [
16] presented a novel end-to-end FD technique using the fine-tuned VMD and CNN mechanisms. An essential proposal is that CNN can be trained only using healthy and single fault data sets, whereas compound faults data from the training phase cannot be utilized. During the testing phase of CNN technique, the intelligent technique alarmed an untrained compound faults’ state, when the developed probability of the CNN outcomes fulfills a group of probabilistic states. In the study conducted earlier [
17], a novel technique was proposed based on RNN to identify the fault types from the RM. In this study, 1D time-series vibration signal was initially converted into 2D images. Next, the GRU was established to exploit the temporal data of time-series data and learn the representative features of the created images. Last, the MLP was utilized to execute the fault detection.
Although DL-based fault diagnosis methods exist abundantly in the literature, there is still a need to design an automated fault diagnosis model with an enhanced detection rate. As the increasing number of DL models can result in model overfitting, optimal hyperparameter selection becomes essential. Since the trial-and-error method for hyperparameter tuning is a tedious and erroneous process, metaheuristic algorithms are applied. Therefore, in this work, SPOA algorithm is deployed for the parameter selection of the BLSTM model.
4. Experimental Validation
The proposed SPOAI-FD technique was experimentally validated by means of automotive gearbox and bearing fault datasets [
23,
24]. The former dataset comprises seven classes whereas the latter dataset includes a total of 10 classes. The first dataset holds seven types of health statuses, such as outer race bearing fault, minor-chipped gear fault, missed tooth gear fault, and three types of compound faults (Normal, Minor-chipped tooth, Missing tooth (0.2 mm), and the Missing tooth (2 mm)). The second dataset has both normal as well as fault data. The bearing fault has a few types, such as the Inner race (IF), Outer race (OF), and Ball faults (BF). Therefore, 10 kinds of bearing health status under varying loads were studied. The details of the dataset are shown in
Table 1.
Table 2 and
Figure 2 show the accuracy examination results achieved by the proposed SPOAI-FD model on gearbox dataset under distinct classes. The results exhibit that the proposed SPOAI-FD method attained better accuracy values for every run. For example, with Class 1, the proposed SPOAI-FD method obtained the accuracy values 0.9941, 0.9936, 0.9937, 0.9945, and 0.9940 correspondingly. Similarly, with Class 2, the presented SPOAI-FD technique attained the accuracy values 0.9915, 0.9927, 0.9935, 0.9914, and 0.9906, correspondingly. Likewise, with Class 3, the SPOAI-FD approach produced the following accuracy values, 0.9920, 0.9932, 0.9926, 0.99145, and 0.9921, correspondingly. Simultaneously, with Class 7, the proposed SPOAI-FD method obtained the accuracy values 0.9946, 0.9903, 0.9924, 0.9945, and 0.9921, correspondingly.
Table 3 and
Figure 3 show the comparative accuracy analysis results attained by the proposed SPOAI-FD and other recent approaches [
25,
26] on gearbox datasets. The result demonstrate that the proposed SPOAI-FD method achieved better accuracy values than the rest of the methods under all the classes. For example, with Class 1, the SPOAI-FD model accomplished a high accuracy of 0.9940 whereas the FFTKNN, FFTSVM, FFTDBN, FFTSAE, CNN, CNN2 and the IIFD-SOIR technique obtained the least accuracy values, such as 0.8364, 0.9886, 0.9746, 0.9855, 0.9885, 0.9881, and 0.9876, correspondingly. Simultaneously, with Class 2, the proposed SPOAI-FD model gained an increased accuracy of 0.9919 although the existing models, such as FFTKNN, FFTSVM, FFTDBN, FFTSAE, CNN, CNN2, and IIFD-SOIR techniques resulted in low accuracy values, such as 0.9195, 0.9801, 0.9693, 0.9836, 0.9821, 0.9797, and 0.9852, correspondingly. Concurrently, with Class 3, the presented SPOAI-FD model accomplished a high accuracy of 0.9923 whereas the other models, such as FFTKNN, FFTSVM, FFTDBN, FFTSAE, CNN, CNN2, and IIFD-SOIR techniques, obtained the least accuracy values, such as 0.9811, 0.9837, 0.9777, 0.9802, 0.9684, 0.9676, and 0.9811, correspondingly.
The accuracy investigation outcomes, obtained by the proposed SPOAI-FD approach, under gearbox dataset, are shown in
Figure 4. The result demonstrates that the proposed SPOAI-FD technique gained an increment in its validation accuracy compared to the training accuracy. Furthermore, it is obvious that the accuracy value becomes saturated based on the count of epochs.
The loss investigation outcomes of the SPOAI-FD system, under gearbox dataset, are illustrated in
Figure 5. The figure reveals that the proposed SPOAI-FD method significantly reduced the validation loss over training loss. Additionally, it is noted that the loss value becomes saturated with the count of epochs.
Table 4 and
Figure 6 depict the accuracy investigation outcomes attained by the proposed SPOAI-FD approach on bearing dataset under diverse classes. The experimental values demonstrate that the proposed SPOAI-FD approach achieved improved accuracy values under all the runs. For example, with Class 1, the SPOAI-FD methodology gained the accuracy values 0.9922, 0.9939, 0.9945, 0.9906, and 0.9906, correspondingly. Likewise, with Class 2, the SPOAI-FD algorithm yielded the following accuracy values, 0.9946, 0.9940, 0.9920, 0.9910, and 0.9922, correspondingly. Moreover, with Class 3, the SPOAI-FD approach accomplished the accuracy values 0.9911, 0.9927, 0.9941, 0.9943, and 0.9937, correspondingly. At last, with class 7, the proposed SPOAI-FD technique reached the accuracy values 0.9923, 0.9927, 0.9933, 0.9930, and 0.9901, correspondingly.
Table 5 and
Figure 7 portray the brief comparison study outcomes accomplished by the proposed SPOAI-FD and other recent approaches on bearing dataset. The simulation values depict that the SPOAI-FD system gained enhanced accuracy values over the rest of the methods under all the classes. For example, with Class 1, the SPOAI-FD model gained increased accuracy values, such as 0.9924, while FFTKNN, FFTSVM, FFTDBN, FFTSAE, CNN, CNN2, and IIFD-SOIR techniques achieved the least accuracy values, such as 0.9706, 0.9892, 0.9815, 0.9860, 0.9791, 0.9833, and 0.9870, correspondingly. Simultaneously, with Class 2, the proposed SPOAI-FD method yielded an enhanced accuracy of 0.9928. However, the other models, such as FFTKNN, FFTSVM, FFTDBN, FFTSAE, CNN, CNN2, and IIFD-SOIR techniques, achieved minimal accuracy values, such as 0.9582, 0.9446, 0.9741, 0.9691, 0.9382, 0.9127, and 0.9771, correspondingly. Concurrently, with Class 3, the SPOAI-FD model accomplished a maximum accuracy of 0.9932. However, the FFTKNN, FFTSVM, FFTDBN, FFTSAE, CNN, CNN2, and the IIFD-SOIR techniques obtained the least accuracy values, such as 0.9721, 0.9862, 0.9836, 0.9864, 0.9792, 0.9934, and 0.9867, correspondingly.
The accuracy investigation outcomes of the SPOAI-FD approach under bearing dataset are depicted in
Figure 8. The result exhibits that the proposed SPOAI-FD methodology gained a better validation accuracy compared to the training accuracy. Additionally, it is noticeable that the accuracy value becomes saturated with the count of epochs.
The loss investigation results of the SPOAI-FD algorithm under bearing dataset are depicted in
Figure 9. The figure reveals that the proposed SPOAI-FD method achieved a reduction in the validation loss than the training loss. Additionally, it is to be noted that the loss value becomes saturated with the count of epochs.
Table 6 provides the overall average analysis results of the SPOAI-FD and other recent methodologies.
Figure 10 offers the comparative average accuracy analysis outcomes of SPOAI-FD approach and other methods on gearbox dataset. The results show that the SPOAI-FD technique outperformed all other methods with maximum training and testing accuracies.
For example, with respect to training accuracy, the SPOAI-FD approach reached a maximum training accuracy of 0.9960. However, other methods such as the FFTKNN, FFTSVM, FFTDBN, FFTSAE, CNN, CNN-2, and IIFD-SOIR algorithms gained lesser training accuracies, such as 0.8567, 0.9753, 0.9711, 0.9864, 0.9764, 0.9726, and 0.9899, correspondingly.
Figure 11 illustrates the detailed average accuracy analysis outcomes achieved by the proposed SPOAI-FD algorithm and other techniques on bearing dataset. The results obtained showcase that the SPOAI-FD method surpassed all other existing techniques with maximum training and testing accuracies. For instance, the proposed SPOAI-FD system reached an increased training accuracy of 0.99510, whereas the other methods such as FFTKNN, FFTSVM, FFTDBN, FFTSAE, CNN, CNN-2, and IIFD-SOIR techniques produced the least training accuracy values, such as 0.9754, 0.9622, 0.9814, 0.9740, 0.9789, 0.9768, and 0.9890, correspondingly. By observing the abovementioned outcomes, it can be inferred that the proposed SPOAI-FD system has an enhanced fault diagnosis efficiency over other methods.