A CLSTM-CNN-Attention hybrid model and its application in fault diagnosis

2.1. Submodules of the hybrid model

The hybrid model consists of three modules, namely CLSTM, CNN, and attention module. LSTM is the enhanced version of recurrent neural network (RNN), which could address the gradient vanishing problem of RNN effectively [24]. LSTM not only can capture the evolving temporal patterns and related distant dependencies of input sequences, but also extract the temporal feature information of input sequences effectively. In this study, a LSTM network as shown in Fig. 1 is used, in which the gate architecture is composed of three distinct gates: the forget gate, the input gate, and the output gate. Based on Fig. 1, it can be seen that the results generated by the three gates are transmitted to a multiplication element respectively, which can control the input and output of information flow and the form of cell units. In the paper, pre-apply a convolutional layer to LSTM to strengthen its anti-interference ability, and the CLSTM structure is constructed. The corresponding mathematical expression of CLSTM could be expressed as follows:

where $Conv$ represents the convolution operation, $X_t$ represents the obtained data by convolving the input data, $c_t$ is the calculation method for a storage unit at the moment $t$, $h_t$ is all output terminals of the storage device system unit at the time$t$, $W$ and $b$ represents the weight value and bias element respectively, $\sigma$ and $\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{h}$ are the excitation functions, $i$, $f$ and $o$ are the computation methods belonging to the three gates at a time $t$.

Fig. 1LSTM structure

The CNN module is one kind of unique deep feedforward neural network, which could avoid parameter redundancy effectively caused by fully connected layers. The CNN module mainly consists of convolutional layers, pooling layers, activation layers, and Dense layers. The function of these four layers is to perform feature learning automatically without relying on any prior knowledge. The one-dimensional vibration data is used as input of the suggested approach, so the one-dimensional CNN is used. Suppose that the input signal and filter are denoted by $x\in T^n$ and $w\in T^m$ respectively, and the corresponding convolution processes could be represented by the following equations:

where $b^l$ is the bias value, $z^l$ serves as the linear activation vector corresponding to the layer $l$, $\sigma$ is the non-linear activation function, and $x^l$ is the layer’s output feature $l$. $Conv$ represents convolution operation and $same$ represents all-zero filling. $Y^l$ refers to the eigenvector after convolution processing, $x^{l-1}$ is the output feature of the layer $(l-1)$, $w^l$ refers to the size of the nucleus of the $l$ layer, $\left(y^l\left(t\right),\cdots ,y^l\left(n-m+1\right)\right)\in T^{n-m+1}$ represents the convolution process, ${\sum }_{i=1}^m{x}^{l-1}\left(t+i-1\right)w^l\left(i\right)$ represents the computation process of the convolution process $y^l\left(t\right)$, $w^l\left(i\right)$ represents the nucleus of the $l$ layer, $x^{l-1}\left(t+i-1\right)$ is the signal of the $(l-1)$ layer.

A new linear rectification function named ELU activation function with a positive hyperparameter is introduced into the constructed hybrid model, which aims to enhance its noise resistance ability. The function expression of ELU is given in Eq. (9):

Eq. (9) corresponding sketch map is shown in Fig. 2. The graph as shown in Fig. 2 has a smaller slope in the negative value region, which makes the model more adaptable to changes of negative interference. Therefore, it can increase the sensitivity of the model to interference, and improve the model’ diagnostic accuracy in complex marine environments.

Fig. 2The sketch map of ELU function

The attention module is built by mimicking the human brain’s mechanisms, which only selects and handles some key input information while processing large amounts of input information, thus improving the diagnosis efficiency. The calculation of the attention module could be represented as follows:

in which $s\left(x_t,h_{t-1}\right)$ is the attention score function, $V$, $W$, $U$ represents the weight matrix inside the neural network, and ${\alpha }_t$ is the attention distribution.

To facilitate reproduction and fair comparison, the micro-architectures of the two parallel branches are summarized in Table 1, and their architectures are depicted in Fig. 3 and Fig. 4.

Fig. 3CLSTM module architecture

Fig. 4CNN branch architecture

2.2. The constructed hybrid model and flow chart of the proposed method

A novel parallel connection method is adopted to connect the above-mentioned three submodules to construct the hybrid model. The structure of the hybrid model is illustrated in Fig. 5. This connection approach not only captures the temporal and spatial fault information of the input signal simultaneously, but also extracts features more comprehensively. Specifically, inspired by reference [25], the designed network model mainly consists of two independent branches. The upper part of Fig.5 shows the constructed CLSTM model, while the lower part shows the CNN model. These two branches can operate independently without interfering with each other, thus effectively extracting the temporal and spatial fault features of the input signal simultaneously. This design method greatly improves the running efficiency of the model and enhances its feature extraction ability, which provides a richer information foundation for subsequent fault diagnosis and prediction. Meanwhile, it reduces feature loss and does not require the training of LSTM and CNN networks separately, thereby saving time and simplifying the training process. In addition, the improved LSTM and CNN have strong expressive powers, allowing the model to describe the fault feature buried in the input signal more accurately: the key features could be assigned higher weights by focusing on the fused temporal and spatial feature information, so the diagnosis accuracy of AUV thruster under interference could be improved. At last, the SoftMax classifier is used to classify and diagnose faults.

The concrete diagnosis processes are as follows based on the proposed hybrid network:

Step 1: The vibration signals of the AUV thruster are collected, and the dataset suitable for network input is established.

Step 2: Construct the CLSTM module by adding a convolution layer before LSTM to improve the anti-interference ability of LSTM.

Step 3: ELU is built into the traditional CNN module to increase the hybrid model’ sensitivity to interference, and its diagnostic accuracy in complex marine environments is improved.

Step 4: The hybrid model named CLSTM-CNN-Attention neural network for interference suppression is constructed by using a parallel connection method according to Fig. 5, and the attention module is built into the hybrid model to improve its efficiency.

Step 5: The dataset is randomly partitioned into the training subset and the testing subset, and diagnosis classification is realized at last by using SoftMax.

The specific configuration of the experimental running environment is as follows: the operating system is Windows 11, the central processing unit is Intel Core i5-12500H, and the programming environment is based on the Tensorflow framework of Python 3.6. To implement the proposed network model, Python code can be used at https://github.com/YXGQ/A-fault-diagnosis-method-for-AUV. Get more detailed information about it.

Fig. 5Structure of the hybrid network

2.3. Network structure and parameter settings

According to the model proposed in this paper, the programming language used in this experiment is Python 3.6. The running configuration is as follows: the operating system is Windows 11. The CPU is Intel Core i5-12500H. The dataset is divided into a training set and a test set in a 7:3 ratio. The network parameters of the CLSTM-CNN-Attention model are shown in Table 2. A parallel branch structure is adopted to extract temporal and spatial features, respectively.

3.1. Data description

To acquire vibration signals reflecting the dynamic characteristics of the thruster, piezoelectric accelerometers (Model: PCB 352C65, Sensitivity: 100 mV/g, Range: ±50 g, Frequency Response: 0.5-10000 Hz) were adopted, which are the standard sensors for vibration engineering of rotating machinery. The sensors were rigidly fixed on the bearing seat of the thruster shell via a magnetic base, a key position for vibration transmission, and calibrated using a standard excitation table (Model: JZK-10) before the experiment to ensure measurement accuracy. The AUV was placed in a static water tank to eliminate the interference of the external flow field during the test. The DH5902N rugged data acquisition system (24-bit sampling precision, 4 input channels, anti-aliasing filter cutoff frequency: 6000 Hz) was used to collect and store the vibration signals. The data sampling parameters were set as follows: the thruster speed was 3000 r/min, the sampling frequency was 12800 Hz, and the sampling duration was 10 s.

To clearly display the hardware configuration, Figs. 6-7 present the physical installation details of the test system. Fig.6 shows the PCB 352C65 acceleration sensor located on the thruster. Fig. 7 illustrates the connection between the DH5902N data acquisition system, sensors, and upper computer (Noting: Figs. 6-7 are taken by the authors in Huanghe University of Science and Technology Rotating Machinery Fault Laboratory on 2.10.2026).

Fig. 6PCB 352C65 Piezoelectric Acceleration Sensor

Fig. 7Physical diagram of the connection between the DH5902N data acquisition system and sensors, as well as the upper computer

A total of 5 kinds of operating states (Normal, Unbalance, Entangle, Chute Loose, and Futaba Unbalance) are simulated and their corresponding signals are collected, and the labels 0-4 correspond to the five kinds of operating states. Each state provided 30 groups of raw vibration signals. A sliding window of 2048 samples with 50 % overlap was applied, yielding 300 segments per state, as summarized in Table 3.

It should be noted that Unbalance (Noted as 0) is simulated by tying a tie to a blade, and Futaba Unbalanced (Noted as 4) is simulated by tying two ties to two blades respectively

To ensure the authenticity and engineering relevance of the experimental data, five typical operating states of AUV thrusters (including one normal state and four artificial fault states) were designed and simulated based on common failure modes in practical marine operations [3, 17]. The specific simulation principles, operation steps, and parameter settings are detailed as follows:

1. Normal (State 3).

No artificial intervention was performed on the AUV thruster. The propeller blades (material: titanium alloy, 4 blades), transmission shaft, chute, and other core components were maintained in the factory-designed standard state. The assembly torque of all fixed bolts was 8 N·m (consistent with the AUV thruster’s rated working parameters), ensuring no eccentric mass, structural looseness, or external interference. This state served as the baseline for normal vibration signal collection.

2. Unbalance (State 0).

This fault simulates the mass eccentricity of the propeller caused by uneven wear, partial corrosion, or attachment of marine debris (e.g., shellfish) during long-term operation. The specific simulation method: A stainless steel washer with a mass of 5 g (diameter: 8 mm, thickness: 2 mm, density: 7.9 g/cm³) was selected as the eccentric load, fixed on the outer edge of the 1st propeller blade (1/3 of the blade length from the tip) using high-temperature resistant and waterproof adhesive tape. The washer was positioned perpendicular to the blade surface to avoid affecting the water flow field, ensuring that the eccentric mass only induces vibration without changing the propeller’s aerodynamic characteristics.

3. Entangle (State 1).

This fault simulates the entanglement of the thruster by external flexible objects (e.g., fishing nets, marine plants, or plastic debris) in the marine environment, which leads to increased rotational resistance and uneven torque. Simulation steps: (1) A nylon rope with a diameter of 2 mm (tensile strength: 50 MPa, length: 50 cm) was selected to mimic common marine entanglement objects. (2) The rope was tightly wound 3 times around the junction of the thruster’s transmission shaft and the protective cover, with a 10 cm free end left to simulate the randomness of real entanglement. (3) The winding tightness was controlled to ensure that the thruster could still operate at the rated speed (3000 r/min) without stalling, which is consistent with the mild-to-moderate entanglement scenario in practical applications.

4. Chute Loose (State 2).

The chute is the key structure for fixing the thruster motor and ensuring the coaxiality of the motor and transmission shaft. This fault simulates the loosening of the chute caused by long-term vibration or bolt fatigue. Simulation method: (1) The four M6 fixing bolts of the chute were loosened from the standard torque (8 N·m) to 3 N·m using a torque wrench. (2) A dial indicator was used to measure the radial runout of the transmission shaft, ensuring the runout range was 0.15-0.2 mm (consistent with the slight loosening fault in engineering practice). (3) No additional displacement was applied to avoid excessive deviation beyond the actual fault range.

5. Futaba Unbalance (State 4).

This fault simulates a more complex propeller imbalance scenario (e.g, uneven wear of two symmetric blades or attachment of debris on multiple blades). Simulation method: Two identical stainless steel washers (each 5 g, same specifications as State 0) were fixed on the 1st and 3rd symmetric propeller blades (1/3 of the blade length from the tip), respectively. The washers were installed in the same direction (perpendicular to the blade surface) to form a bidirectional eccentric mass system, which induces more complex vibration characteristics than single-blade imbalance.

The experiment was carried out in a static water tank, which can eliminate the interference of the external flow field and accurately obtain the pure vibration characteristics of the thruster under different fault states. However, the static water tank cannot simulate the complex marine background noise (e.g., wave noise, ship noise, biological noise), so digital simulation of Gaussian noise is adopted to make the experimental conditions closer to the actual marine environment. Each state was tested under the same environmental conditions (static water tank, temperature: 25 ± 1°C, water depth: 1.5 m) to eliminate the influence of external environmental factors on the experimental results.

As is well known, there are many interfering factors in the complex ocean environment, including natural disturbances such as oceanic noise, biological noise, earthquake noise, rainfall noise, etc. Mankind interference includes noise from navigation, industry, drilling, and other sources. In reference [26], a summary of these two types of interference was conducted, and it was found that the vast majority of interference ranges from 0 to150 decibels (dB). Gaussian noise in texture noise is the most effective way to simulate natural and human interference. In the marine environment, noise from various sources (such as wave activity, wind speed changes, etc.) typically exhibits characteristics of an approximate Gaussian distribution. Therefore, using Gaussian noise can reasonably simulate the ocean background noise that may occur under specific conditions. Therefore, we conducted experimental verification through six different signal-to-noise ratios of 2 dB, 10 dB, 50 dB, 100 dB, 150 dB, and None.

Gaussian noise was digitally generated using NumPy:

where $\sigma$ is determined by the desired SNR via $\sigma =\frac{RMS\left(signal\right)}{10^{(SNR/20)}}$.

The noise sequence was first energy-normalized to unit RMS and then scale-multiplied by $\sigma$. Then it was sample-wise added to the clean vibration signal:

Fig. 8Typical vibration signals of five operating states: a) 0-Unbalance, b) 1-Entangle, c) 2-Chute Loose, d) 3-Normal, e) 4-Futaba Unbalance, and f) comparison of original signal (blue) and signal with 2 dB Gaussian noise (red) for the Entangle state

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To verify the anti-interference capability of the proposed model in complex marine environments, Gaussian white noise is added through digital simulation. Interferences such as waves and ship navigation in marine environments typically follow an approximate Gaussian distribution. Therefore, Gaussian noise is employed to effectively simulate actual ocean background noise.

The specific steps for noise addition are as follows:

1) SNR calculation: Calculate the noise standard deviation based on the target signal-to-noise ratio SNR (dB): $\sigma =\sqrt{P_{signal}/10^{SNR/10}}$, where $P_{signal}$ is the original signal power, $P_{signal}=\frac{1}{N}{\sum }_{i=1}^N{x}_i^2$.

2) Noise generation: Generate a standard normal distribution random sequence $N\left(\mathrm{0,1}\right)$, multiplied by $\sigma$ to obtain Gaussian noise $n\left(t\right)$ with the target power.

3) Signal superposition: Superimpose the noise onto the original signal: $x_{noisy}\left(t\right)=x\left(t\right)+n\left(t\right)$.

Six SNR levels are set in this experiment: None, 150 dB, 100 dB, 50 dB, 10 dB, and 2 dB. Fig. 8(f) shows the signal comparison of the Entangle state at 2 dB SNR, where it can be observed that strong noise has severely masked the characteristics of the original signal, thereby verifying the diagnostic capability of the model under extreme working conditions.

Note: The noise generation is based on the measured vibration signals. A fixed random seed is used to ensure repeatability. The experimental data are all derived from the static water tank measurement data collected by the PCB 352C65 sensor, and are not pure computer simulation data sets.

3.2. Result analysis

Interference resistance analysis: the analysis result by using the proposed hybrid model is compared with the results of WDCNN [27], LSTM, and CNN respectively to verify its advantage. WDCNN is a very classic deep convolutional neural network model, which is characterized by the use of a wide first kernel design, and it can grasp the global features of the input signal effectively. In addition, WDCNN also has good noise suppression ability, which can maintain high accuracy and robustness while processing noisy data. Therefore, it is used for comparison in the paper. Furthermore, the collected unprocessed vibration data is used as input. The training dataset and test dataset are divided randomly, with a total of 1380 training samples and 590 testing samples being obtained. It should be noted that 10 experiments on all four methods were conducted respectively to eliminate accidental errors.

Fig. 9Diagnostic precision of various methods under different levels of signal-to-noise

Fig. 9 presents the experimental outcomes of each model under the six diverse levels of signal-to-noise. Through comparison, it could be found that the suggested method achieved the best outcomes: the diagnostic precision of the proposed approach is capable of attaining high scores when there is no noise influence or the ratio of signal to noise reaches a relatively high echelon, that is, 150 dB. The diagnostic precision of the proposed method still could reach 95.96 % when the signal-to-noise ratio is extremely minor, that is 2 dB. The execution of the proposed method is superior to the other three methods in both of the above mentioned extreme signal-to-noise ratios, and the aforesaid analysis verifies that the proposed method exhibits favorable interference suppression capability.

Convergence speed analysis: the convergence speed of an intelligent diagnosis model is one important criteria for evaluating its quality. To verify the superiority of the proposed model in convergence speed, visual comparative analysis is conducted on the accuracy curve (ACC) and loss curve (Loss) under 50 dB signal-to-noise level. The pertinent experimental findings are capable of being referred to Fig. 10 and Fig. 11, based on which it could be noticed that the presented approach achieved the utmost accuracy and lowest loss after 60 iterations, and its convergence speed is also better than the other three methods, which also reaches a steady state firstly after 60 iterations.

Fig. 10Diagnostic accuracy of various methods under 50 dB signal-to-noise level

Fig. 11Diagnostic loss of various methods at 50dB signal-to-noise level

Stability performance analysis: multiple experiments are carried out to confirm the stability functioning of the suggested model, and the accuracy radar chart of multiple experiments of each model with the 50 dB signal-to-noise level is presented in Fig. 12. Based on Fig. 12, it is evident that the suggested approach has achieved the most stable outcomes across numerous experiments and exhibits the minimum error. Specifically, the average accuracy of the suggested method is 99.66 %, which improves the average accuracy by 20.29 %. Compared with the WDCNN method and the CNN method, the accuracy is improved by 20.69 % and 47.25 % respectively. This further means that the proposed method exhibits stronger anti-interference ability.

Classification accuracy analysis: Fig. 13 shows the three-dimensional confusion matrix of various methods under 50 dB signal-to-noise level, based on which the classification accuracy and misclassification accuracy of each type of fault can be seen clearly. Among them, the main matrix diagonal corresponds to the classification accuracy of each type of fault, while the two sides of the main diagonal are the misclassification accuracy of each type of fault. The color depth in the confusion matrix corresponds to the precision color bar on the right side of the graph, which can reflect the classification accuracy more intuitively. Based on the results as illustrated in Fig. 13, the proposed approach shows excellent effectiveness in both classification accuracy and misclassification.

Fig. 12The stability of multiple experiments of various methods under 50 dB signal-to-noise level

Fig. 13Confusion matrices of various methods at 50 dB signal-to-noise level: a) Proposed method; b) WDCNN; c) LSTM; d) CNN

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Fig. 14 displays the visualization outcomes of the final layer output of the four methods. It is noticeable that the put-forward method has the capacity of effectively aggregating the target sample together and possessing good separability. This further validates the effectiveness of the put-forward approach in extracting and representing classification information.

Fig. 14Visualization analysis of various methods at 50 dB signal-to-noise level by t-SNE: a) Proposed method; b) WDCNN; c) LSTM; d) CNN

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Table 4 shows that the proposed hybrid model achieves the highest accuracy and maintains balanced Precision and Recall, yielding the largest F1-score. The AUC approaching 1 indicates almost perfect separability between fault classes even under 50 dB ocean interference.

In addition, we conducted a thoroughly comparative analysis between the proposed method and the state-of-the-art fault diagnosis methods for Haiwei-1 AUV. Similarly, the comparative analysis is also conducted under a disturbance level of 50dB. The following state-of-the-art methods are included: a multi-scale dilated convolutional network named as MDACNN [28], a network based on support vector glow encoding description (SVGED) named as SVGED [29] for anomaly detection of AUV thrusters, a squeeze excitation (SE) attention residual network (SEResNet) [30]. As shown in Fig. 15, the proposed method demonstrated excellent performance under 50 dB interference conditions, demonstrating extremely high accuracy and stability, significantly outperforming the other three compared methods. Specifically, MDACNN, SVGED, and SEResNet perform significantly poorly under 50 dB interference, with large fluctuations in accuracy and stability. The above comparison result indicates that the proposed method has a stronger ability to cope with the impact of external interference. Although the compared methods can achieve relatively good results in some cases, their performances are significantly suppressed while encountering strong interference, and their performances are also decreased compared with the non-interference situation.

Fig. 15Comparison between the proposed method and the advanced method

3.3. Engineering application value and implementability analysis

The proposed method is designed to solve practical engineering problems of AUV thruster fault diagnosis, with clear implementability and application prospects in marine engineering. Key engineering-oriented advantages are as follows: