# Unsupervised Time Series Fault Detection on Marine Dual Fuel Engines Exhaust System using LSTM-AE

Ayah Youssef, Hassan Noura, Hassan Dabaja, El-Mostafa El-Adel, Mustapha Ouladsine

► To cite this version:

Ayah Youssef, Hassan Noura, Hassan Dabaja, El-Mostafa El-Adel, Mustapha Ouladsine. Unsupervised Time Series Fault Detection on Marine Dual Fuel Engines Exhaust System using LSTM-AE. The 33rd Mediterranean Conference on Control and Automation, Jun 2025, Tangier, Morocco. pp.72-77, 10.1109/MED64031.2025.11073222. hal-05065731

HAL Id: hal-05065731

https://amu.hal.science/hal-05065731v1

Submitted on 6 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Open licence - etalab
# Unsupervised Time Series Fault Detection on Marine Dual Fuel Engines
Exhaust System using LSTM-AE*

Ayah Youssef<fnref n="1" />, Hassan Noura<fnref n="1" />, Hassan Dabaja<fnref n="1" />, El-Mostafa El-Adel<fnref n="1" />, Mustapha Ouladsine<fnref n="1" />

**Abstract**— Fault diagnosis in marine dual fuel systems is essential due to the complex operating conditions and the potential for significant safety and environmental impacts. This paper uses Long Short-Term Memory Autoencoder (LSTM-AE) for fault detection in the exhaust systems of marine dual fuel engines. The LSTM-AE model is designed to detect anomalies in exhaust valve closing dead time (ECDT) using real historical data. The methodology involves creating input sequences, encoding and decoding them using LSTM layers, and detecting faults based on reconstruction errors. The model is trained on healthy data from Cylinder 3 and tested on other cylinders, achieving a precision of 1.00, recall of 0.82, and F1-score of 0.90. Results showed that the model successfully detects both known and unreported faults, demonstrating its potential for real-time fault monitoring in marine propulsion systems.

## I. INTRODUCTION

Fault detection in marine dual-fuel engines is challenging due to high variability in operating conditions, transient loads, and complex failure mechanisms. Traditional fault detection methods, such as rule-based diagnostics or threshold-based monitoring, struggle to adapt to these dynamic environments. Marine engines operate under transient and variable conditions, making fault detection challenging [1]. The presence of multiple excitation sources complicates the extraction of fault features [2]. Timely fault diagnosis helps in identifying issues before they escalate, thus preventing engine failures that could lead to maritime accidents [3]. Intelligent systems facilitate continuous monitoring of engine performance, allowing for immediate corrective actions when anomalies are detected [4]. Accurate fault detection in fuel delivery systems can significantly reduce harmful emissions, contributing to cleaner marine operations [5]. Effective diagnostics ensure that vessels meet environmental standards, thus promoting sustainable practices in the maritime industry [6]. The safety of exhaust systems in marine engines is crucial for both environmental protection and operational efficiency. The exhaust system is designed to manage exhaust gases effectively, ensuring that harmful emissions are minimized and that the engine operates within safe parameters. Recent research has explored machine learning models such as Support Vector Data Description (SVDD) [5] and Convolutional Neural Networks (CNNs) [2] for engine diagnostics. However, these methods require large labeled datasets, which

are often unavailable in marine applications. Unsupervised learning, particularly LSTM-based Autoencoders (LSTM-AE), provides an alternative by detecting anomalies without prior fault labels. This study develops an LSTM-AE model to detect faults in exhaust valve closing dead time (ECDT), leveraging time-series data collected from a real marine dual-fuel engine. First, Section II provides a comprehensive overview of the exhaust valve operation. Then, the theoretical background of LSTM and autoencoders (AE) is introduced in Sections III and IV, respectively. The developed LSTM-AE diagnostic methodology is presented in Section V, along with a detailed explanation of the data and preprocessing steps used for the model in section VI. Finally, the results are discussed in Section VII, and conclusions are drawn in Section VIII.

## II. EXHAUST SYSTEM

The marine dual-fuel engine is essential for vessel propulsion and electricity generation, consisting of five main subsystems: the injection system (diesel or gas with a pilot system), exhaust system (removes burnt gases and powers the turbocharger), air intake system (supports scavenging and cylinder air supply), lubrication system, and cooling system (prevents overheating and mechanical wear). The exhaust system plays a crucial role in combustion efficiency by evacuating gases and driving the turbine for air intake. Its key component, the exhaust valve (EV), regulates gas flow to the manifold and operates under the control of the Engine Control System (ECS), which commands its opening and closing based on the crank angle.

### A. Exhaust Valve Mechanism

The exhaust valve opens and closes by

1) **Opening Process:**

At a desired crank angle, the ECS generates an impulse signal commanding the rail valve directing the servo oil to the lower chamber of the exhaust valve actuator, moving it upward, enabling the exhaust valve to open (see Fig. 1).

2) **Closing Process:**

Once combustion gasses are fully expelled from the cylinder and the desired angle of the crank angle is reached the ECS commands the rail valve. The actuator then returns to its original position due to the force of a mechanical spring, allowing oil to flow out of the actuator (see Fig. 1).

3) **Exhaust Valve Timing:**

The time lag between which the command is sent

<footnote>*This work is supported by BPIFrance, Banque Publique d'Investissement, France.</footnote>

<footnote n="1">¹Aix Marseille Univ., CNRS, LIS (UMR 7020), Avenue Escadrille Normandie-Niemen, F-13397 Marseille Cedex 20, France. (ayah.youssef, hassan.noura, hassan.dabaja, el-mostafa.el-adel, mustapha.ouladsine)@lis-lab.fr</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1. Exhaust valve mechanism</figcaption></figure>

by the ECS to the servo valve and the exhaust valve actually moves is called the dead time.

## B. Exhaust Valve Timing Faults

The delay or advance of the exhaust valve opening or closing dead time will lead to the advance and retard of the opening and closing leading to the reduction of the scavenging air entry quantity in case of advance closing, combustion deterioration, increasing toxic emissions and fuel consumption [7] Showing the importance of detecting faults in closing dead time. Optimizing valve timing can significantly enhance performance and reduce emissions [8].

## III. LSTM

Long Short-Term Memory(LSTM) networks are an advanced type of Recurrent Neural Network (RNN) enabling the learning of long and short-range dependencies by mitigating and vanishing gradient problems. Traditional RNNs are limited in the ability to retain past information, this capability is extended by incorporating a structured memory mechanism allowing selective retention and information forgetting.

An LSTM unit is composed of a memory cell and three primary gates as shown in Fig. 2, the forget, input and output gates. These gates control the information flow through the network, retaining necessary past information and discarding irrelevant data. The two main key LSTMs unit states are:

* Cell State ($C_t$): Updated each time step storing the Long-term memory.

* Hidden State ($H_t$): Used for output at each time step representing the short-term memory.

The LSTM cell processes information sequentially through the following three stages:

The Long Short-Term Memory (LSTM) network processes sequential data through three key gates: the Forget Gate, Input Gate, and Output Gate, ensuring efficient information retention and update across time steps.

**Step 1: Forget Gate** Determines which parts of the previous memory state $C_{t-1}$ should be retained or discarded based on the new input $X_t$ and the previous hidden state $H_{t-1}$. A sigmoid activation function ensures that values range between 0 (completely forget) and 1 (fully retain):

$$f_t = \sigma(W_f[H_{t-1}, X_t] + b_f) \quad (1)$$

The computed forget gate values are then element-wise multiplied with $C_{t-1}$ to selectively retain useful information.

<figure><img src="image_3.png" /><figcaption>Fig. 2. LSTM unit gates</figcaption></figure>

**Step 2: Input Gate** Controls how much new information should be stored in the memory cell. First, a candidate memory update vector $\tilde{C}_t$ is generated using the tanh activation function:

$$\tilde{C}_t = \tanh(W_c[H_{t-1}, X_t] + b_c) \quad (2)$$

A sigmoid-activated input gate determines which components of $\tilde{C}_t$ should be added to the memory:

$$i_t = \sigma(W_i[H_{t-1}, X_t] + b_i) \quad (3)$$

The updated cell state, representing long-term memory, is then computed as:

$$C_t = f_t \odot C_{t-1} + i_t \odot \tilde{C}_t \quad (4)$$

**Step 3: Output Gate** Determines the next hidden state $H_t$ based on the updated cell state $C_t$. A filter vector is computed using a sigmoid activation function:

$$o_t = \sigma(W_o[H_{t-1}, X_t] + b_o) \quad (5)$$

The updated hidden state is then calculated by applying tanh activation to the new cell state and performing element-wise multiplication with $o_t$:

$$H_t = o_t \odot \tanh(C_t) \quad (6)$$

The updated cell state $C_t$ and hidden state $H_t$ are passed to the next LSTM unit, ensuring sequential dependencies are maintained throughout the processing of time-series data.

## IV. AUTOENCODER

Autoencoders are a class of NNs used for unsupervised learning to discover efficient representations of input unlabeled data. They are trained to reconstruct samples by encoding them into lower dimensional representation and then decoding them back to the original format. This process enables the network to learn meaningful features while eliminating irrelevant information or noise.

A standard autoencoder is comprised of three key components as shown in Fig. 3:

* Encoding Network - compresses input data into a lower-dimensional representation.

* Decoding Network - reconstructs the input from the compressed representation.
* Reconstruction Loss - measures the discrepancy between the original input and the reconstructed output.

<figure><img src="image_4.png" /><figcaption>Fig. 3. Standard AE architecture</figcaption></figure>

### Step1: Encoding Operation

The encoder transforms the input $x$, a high-dimensional vector in $\mathbb{R}^m$, into lower dimensional latent representation $h$ preserving the most relevant information, commonly known as the bottleneck layer. This transformation is expressed by the activation function $f_1$ introducing non-linearity.

$$h = f_1(W_i x + b_i) \quad (7)$$

$W_i$ and $b_i$ are the weight matrix and bias vector of the encoder of the encoder.

### Step2: Decoding Operation

The decoder reconstructs the input $x$ from the latent vector representation $h$. The reconstructed output $\hat{x}$ is given by

$$\hat{x} = f_2(W_j h + b_j) \quad (8)$$

Where $f_2$ is the decoder activation function, $W_j$ and $b_j$ are the decoder's weight matrix and bias respectively. Note that the decoder parameters $W_j$ and $b_j$ are not necessarily related to the encoder parameters $W_i$ and $b_i$.

### Step3: Reconstruction Loss

To assess how well the AE reconstructs input samples, a reconstruction loss function is computed. This loss quantifies the difference between the original input $x$ and the reconstructed output $\hat{x}$ using the mean square error (MSE) function.

$$L_{MSE} = \frac{1}{n} \sum_{t=1}^{n} (x_t - \hat{x}_t)^2 \quad (9)$$

where $n$ is the number of samples in the training dataset.

## V. METHODOLOGY

In this section the methodology proposed for anomaly detection is presented. LSTM-AEs are a powerful approach for time-series anomaly detection, due to the ability of LSTMs to capture temporal dependencies. The architecture is trained on a data span where the system is acting without faults, where in case of anomalous data is processed the model

will not be able to reconstruct the output well, showing higher reconstruction error. The overall architecture consists of four main steps: sequence creation, LSTM encoder, LSTM decoder, and anomaly detection.

### A. LSTM-AE

The methodology architecture is represented in Fig. 4. The autoencoder is used to learn efficient representations of input data through an encoding-decoding mechanism. The LSTM-AE leverages the capabilities of both long short-term memory networks and autoencoders, making it well-suited for time-series anomaly detection. It processes sequential data preserving temporal dependencies across multiple time steps. It gradually compresses the high-dimensional input into a lower-dimensional representation, known as the latent vector. The LSTM decoder then reconstructs the original input sequence from this reduced representation. By comparing the reconstructed output with the original input, the reconstruction error is computed. This error is used to establish a threshold, where deviations beyond this threshold based on a trend analysis indicate potential anomalies. The

<figure><img src="image_5.png" /><figcaption>Fig. 4. LSTM-AE architecture</figcaption></figure>

LSTM Autoencoder (LSTM-AE) structure follows a three-step process to detect anomalies in the exhaust valve closing dead time (ECDT).

**Step 1: Creating Input Sequences** – The time-series data is structured into overlapping sequences using a sliding window approach. Each sequence consists of 10 time steps (100 seconds) and 3 features (speed, mode, dead time), forming a 3D input tensor of shape (batch size, 10, 3). This ensures the model captures temporal dependencies effectively while maintaining computational efficiency.

**Step 2: LSTM Encoder** – The input sequences are processed by a two-layer LSTM encoder with 64 and 32 units, respectively. The first LSTM layer extracts temporal dependencies, passing the processed sequences to the second LSTM layer, which refines the learned patterns. The final hidden state of the second LSTM layer is retained, creating a 2D latent representation of shape (batch size, 32). To prepare for decoding, a Repeat Vector layer expands this latent space back to a 3D tensor of shape (batch size, 10, 32), ensuring the temporal structure is maintained for reconstruction.

**Step 3: LSTM Decoder** – The decoder reconstructs the original sequence from the latent representation using two LSTM layers, with 32 units in the first layer and 64 units in the second layer.
the second. The first layer processes the (batch size, 10, 32) tensor, passing the output to the second layer, which refines the sequence representation to (batch size, 10, 64). To restore the original format, a TimeDistributed layer applies a fully connected transformation across all time steps, reducing the output shape to (batch size, 10, 3), ensuring the reconstructed sequence matches the original input. This structure enables the model to effectively learn temporal dependencies and reconstruct healthy patterns for anomaly detection in ECDT.

**Step4: Fault Detection-** A fault is identified when an observation deviates from healthy behavior. One common technique for fault detection is setting a threshold, where any observation exceeding this limit is classified as an anomaly. However, to minimize false positive alarms, the fault must persist above the defined threshold for a consecutive number of time steps. Once the healthy dataset is trained and reconstructed, the Mean Squared Error (MSE) (Equation 9) is computed across all training time steps. The maximum MSE value observed during training is used as the anomaly detection threshold. For detecting anomalies in Exhaust Valve Closing Dead Time (ECDT), this threshold is set to 0.005. Additionally, the anomaly must remain above this threshold for 5 minutes, corresponding to 30 consecutive records, to be considered a confirmed fault.

<figure><img src="image_6.png" /><figcaption>Fig. 5. LSTM-AE diagnosis architecture</figcaption></figure>

## VI. DATA AND DATA-PREPROCESSING

The Exhaust Closing Dead Time (ECDT) in a DF engine is affected by several factors other than the ECS such as the shaft speed and fuel mode. The closing and opening is directly related to the position of the crank valve which in terms is related directly to the shaft speed, where higher rpms mean more cycles per minute; the engine from which data was collected operates at a maximum of 80rpm ≡ 13.3cycles/min. This indicates that each cycle is completed faster with engine speed increase indicating lower deadtime values for faster cycle completion. As well as changing the engine fuel mode impacts the dead time where the combustion in diesel mode differs from gas mode. This behaviour is well shown in Fig. 6. The model aim is to learn

this behaviour of ECDT vs shaft speed and fuel mode from one healthy cylinder.

<figure><img src="image_7.png" /><figcaption>Fig. 6. Cylinder 3 vs Fuel Mode and Shaft speed</figcaption></figure>

<table><caption>TABLE I
REPORTED FAULTS ON ECDT</caption><thead><tr><td>Date</td><td>Fault Report Summary</td></tr></thead><tbody><tr><td>26-08-2021</td><td>Experienced a random alarm on the main engine (a dozen times over a month and a half) due to exhaust valve #1 timing failure. Symptoms did not match expected trends of open and close dead time. Suspected an electrical issue on other cylinders. No improvement after multiple replacements; alarm reappeared three days ago.</td></tr><tr><td>04-09-2021</td><td>Due to high closing dead time and timing failure, the exhaust valve was replaced with a spare. Scratches were found on the exhaust valve spindle and guide bush, indicating significant wear and damage.</td></tr><tr><td>20-09-2021</td><td>Abnormal closing dead time detected for exhaust valves #1, 2, and 5. The ship engineer (SE) must overhaul these valves as soon as possible. If not feasible, three new exhaust valves must be delivered.</td></tr></tbody></table>

### A. Dataset

The Data is collected on the date span between August and the end of January 2022 for a DF engine where several faults are reported (check Table I). Data used in this study is divided into training data and testing data sampled every 10sec, the model is training based on one healthy cylinder number 3 in September (see Fig. 5) where this cylinder showed healthy behaviour with no reported faults. The model is trained on the data span of September 2021 (see Fig. 6) using the data listed in Table II. Testing is performed on the other cylinders, each individually combined with shaft speed and engine fuel mode data.

<table><caption>TABLE II
TRAIN AND TEST DATASET</caption><thead><tr><th colspan="2">Train Data</th><th colspan="2">Test Data</th></tr><tr><th>Variable</th><th>Unit</th><th>Variable</th><th>Unit</th></tr></thead><tbody><tr><td>ENN CYL03 Diesel Exhaust Valve Closing Dead Time</td><td>msec</td><td>ENN CYL01/.../12 Diesel Exhaust Valve Closing Dead Time</td><td>msec</td></tr><tr><td>GTT Shaft rpm</td><td>rpm</td><td>GTT Shaft rpm</td><td>rpm</td></tr><tr><td>ENN EMS1 Gas Fuel Mode</td><td>-</td><td>ENN EMS1 Gas Fuel Mode</td><td>-</td></tr><tr><td>September 2021</td><td></td><td>August-October 2021</td><td></td></tr></tbody></table>

### B. Data preprocessing

1) Sensor Outliers: The ECDT contains high spike values due to electrical or sensor errors which will affect the
training and testing evaluation causing false alarms and bad learning. While the shaft speed data contains negative values representing the time when the engine turns off resulting in inverse turn of the shaft. The negative values are clipped and replaced by a value of 0 while the high values (>190msec) in ECDT are clipped and replaced with an interpolated value as shown in Fig. 7 and 8.

<figure><img src="image_8.png" /><figcaption>Fig. 7. Shaft speed negative values clipping</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 8. ECDT sensor outliers cleaning</figcaption></figure>

2) **ECDT Filtering:** The Savitzky-Golay filter is a digital filtering technique used to smooth noisy signals while preserving important features such as peak and edges. Savitzky-Golay filter applies a least-squares polynomial fitting over a defined window, ensuring minimal distortion of the signal, unlike the moving average filters, which tend to blur signal features. The filter is applied on the 12 cylinders ECDT data.

<figure><img src="image_10.png" /><figcaption>Fig. 9. Cyl01 ECDT Savitzky-Golay filtered signal.</figcaption></figure>

The window length is set to 360 points, and a third degree polynomial is used for local fitting. The smoothed signal is computed using the equation:

$$y'_{i} = \sum_{j=-m}^{m} c_{j} y_{i+j} \quad (10)$$

where $y'_{i}$ represents the smoothed value at index $i$, $y_{i+j}$ are the original noisy data points within the window, $c_{j}$ are the convolution coefficients derived from the least-squares polynomial fit, and $m = \frac{\text{window length} - 1}{2}$ is the half-window size.

The filter is applied to each cylinder's time series using interpolation mode, which helps estimate values and boundary points more effectively. The results are visualized in Fig. 9.

3) **Normalization:** Data normalization is applied to eliminate the impacts of different scales of features readings which reduces the execution time and computational complexity of the training model. A standard scaler normalization as shown is used.

$$Z_{i} = \frac{X_{i} - \mu}{S} \quad (11)$$

where $Z_{i}$ denotes the numeric values ranging between [0,1]; $X_{i}$ indicating a data point while $\mu$ and $S$ refer to the mean and standard deviation.

## VII. RESULTS

By training the model using the dataset in Table II and the hyper-parameters in Table III the model showed excellent reconstruction with a maximum MSE =0.005, the reconstructed signal of cylinder's 3 ECDT is shown in Fig. 10. The threshold is chosen based on the maximum MSE reached by training ECDT healthy data. A fault is detected if the MSE value of testing other cylinders' ECDTs exceeds the value of 0.005 (maximum training MSE) for at least 5 min to satisfy online monitoring.

<table><caption>TABLE III
HYPERPARAMETERS OF PROPOSED METHODOLOGY</caption><thead><tr><td>Hyperparameters</td><td>Values</td><td>Description</td></tr></thead><tbody><tr><td>Sequence length</td><td>10</td><td>No. of data records input</td></tr><tr><td>Batch size</td><td>16</td><td>No. of samples in one pass</td></tr><tr><td>Epoch</td><td>5</td><td>No. of one pass of all samples</td></tr></tbody></table>

<figure><img src="image_11.png" /><figcaption>Fig. 10. Reconstructed vs original signal of Cylinder 3 ECDT after training</figcaption></figure>

The results of testing the LSTM-AE model individually on each cylinder using the data listed in Table II are summarized in Table IV, which compares the detected faults with the reported faults for each cylinder, indicating the start time and whether it corresponds to a reported fault. The model successfully detected 9 faults corresponding to the reported fault dates and identified 3 additional unreported true faults. Figures 11 and 12 illustrate the MSE reconstruction error for cylinders 01 and 06, where the red zone highlights the fault
<figure><img src="image_12.png" /><figcaption>Fig. 11. Cyl01 ECDT reconstruction error</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 12. Cyl06 ECDT reconstruction error</figcaption></figure>

<table><caption>TABLE IV
REPORTED AND UNREPORTED DETECTED FAULTS START DATE</caption><thead><tr><th colspan="5">Cylinder 1</th></tr></thead><tbody><tr><td>2021-08-07<br>18:41:50</td><td>2021-08-14<br>06:36:50</td><td>2021-08-28<br>11:40:40</td><td>2021-09-04<br>10:45:30</td><td>2021-09-16<br>19:22:00<br>2021-09-21<br>00:56:30<br>Fault 3</td></tr><tr><th colspan="5">Fault 1</th></tr><tr><th colspan="5">Cylinder 2</th></tr><tr><td>2021-08-28<br>16:39:40<br>Fault 1</td><td>2021-09-09<br>18:48:20<br>Unreported Fault</td><td>2021-09-16<br>14:37:20<br>Fault 3</td><td>2021-09-23<br>05:41:00<br>Unreported Fault</td><td></td></tr><tr><th colspan="5">Fault 2</th></tr><tr><th colspan="5">Cylinder 5</th></tr><tr><td>2021-08-07<br>15:29:50<br>Fault 1</td><td>2021-08-14<br>07:24:20</td><td>2021-09-07<br>04:54:00<br>Unreported Fault</td><td>2021-09-20<br>17:21:10<br>Fault 3</td><td></td></tr><tr><th colspan="5">Fault 3</th></tr><tr><th colspan="5">Cylinder 7</th></tr><tr><td>2021-08-05<br>22:43:40<br>Fault 1</td><td>2021-08-27<br>17:25:20</td><td></td><td></td><td></td></tr><tr><th colspan="5">Cylinder 11</th></tr><tr><td>2021-08-28<br>18:08:10<br>Fault 1</td><td></td><td></td><td></td><td></td></tr><tr><th colspan="5">Cylinder 3,6,8,9,12</th></tr><tr><th colspan="5">No detected faults</th></tr></tbody></table>

detection period. The model performance was satisfactory compared to the fault reports, as the unreported cylinders (cylinders 3, 6, 8, 9, and 12) were classified as healthy in the model testing results for August–September 2021. In total, 9 faults were correctly detected as true positives, while 3 real faults were identified as false negatives. Based on this analysis, the model’s performance was evaluated using three metrics: **Precision**, given by $P = \frac{TP}{TP+FP} = \frac{14}{14+0} = 1.00$; **Recall**, calculated as $R = \frac{TP}{TP+FN} = \frac{14}{14+3} = 0.8235$; and

**F1-score**, computed as $F1 = 2 \times \frac{P \times R}{P+R} = 0.9032$. Here, TP (True Positives) represents correctly identified detections (both healthy and faulty), FN (False Negatives) refers to unreported detections assumed to be real faults, and FP (False Positives) accounts for incorrect detections. While the model successfully detected all reported faults, it cannot be conclusively stated that it is sensitive to all ECDT faults due to the limited testing and the number of reported fault scenarios.

## VIII. CONCLUSION

This study presented an unsupervised LSTM-AE model for detecting exhaust valve closing dead time (ECDT) anomalies in a marine dual-fuel engine. The model was trained on healthy data and tested on multiple cylinders, achieving a high fault detection rate with an F1-score of 0.90. The results indicate that LSTM-AE can effectively capture time-series anomalies, making it a promising tool for real-time marine engine diagnostics. However, certain limitations persist, including a limited number of recorded fault scenarios and high sampled rate data leading to data gaps, affecting the robustness and efficiency of diagnosis. Future work will focus on enhancing the methodology by optimizing hyperparameters and comparing LSTM-AE with alternative diagnostic techniques for faster and more accurate fault detection. Additionally, efforts will be made to adapt the model for real-time online monitoring, ensuring its practical application in marine propulsion system diagnostics.

## REFERENCES

[1] Yao, Z., Chen, Y., Muhammad, G., Xia, Q. and Yang, C., 2024. Comprehensive investigation of a novel multi-segment heat exchanger for PCM/MF based ocean thermal engine: Numerical analysis, model reduction and experimental work. Applied Thermal Engineering, 248, p.123103.

[2] Li, C. and Cui, D., 2024. Fault Diagnosis of Marine Diesel Engine Based on Multi-scale Time Domain Decomposition and Convolutional Neural Network. Polish Maritime Research, 31(3), pp.85-93.

[3] Youssef, A., Noura, H., Amrani, A.E., Adel, E.M.E. and Ouladsine, M., 2024. A Survey on Data-Driven Fault Diagnostic Techniques for Marine Diesel Engines. arXiv preprint arXiv:2404.10363.

[4] Mendonça, L., Vieira, S.M. and Sousa, J.M.C., 2024, June. INTELLIGENT FAULT DIAGNOSIS OF MARINE EQUIPMENTS. In Maritime Transport Conference (No. 10). Universitat Politècnica de Catalunya. Iniciativa Digital Politècnica.

[5] Wrzask, K., Kowalski, J., Le, V.V., Nguyen, V.G. and Cao, D.N., 2024. Fault detection in the marine engine using a support vector data description method. Journal of Marine Engineering & Technology, pp.1-11.

[6] Lv, Y., Yang, X., Li, Y., Liu, J. and Li, S., 2024. Fault detection and diagnosis of marine diesel engines: A systematic review. Ocean Engineering, 294, p.116798.

[7] G. Zhu, L. Huang, J. Yin, W. Gai, and L. Wei, "Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms," Science Progress, vol. 106, no. 4, p. 00368504231212765, 2023.

[8] Zhang, H., Long, W., Xiao, G., Tian, J., Li, B., Wang, D. and Qian, Y., 2023. Experimental Study of the Influences of Operating Parameters on the Performance, Energy and Exergy Characteristics of a Turbocharged Marine Low-Speed Engine. Processes, 11(10), p.2924.