AI-Driven Predictive Maintenance Tool for Aircraft Engines,Java

```java
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Random;

public class AircraftEnginePredictiveMaintenance {

    // Engine Data Class (Simulated)
    static class EngineData {
        double temperature;
        double pressure;
        double vibration;
        double oilLevel;
        boolean isFaulty;  // Simulated fault indicator

        public EngineData(double temperature, double pressure, double vibration, double oilLevel, boolean isFaulty) {
            this.temperature = temperature;
            this.pressure = pressure;
            this.vibration = vibration;
            this.oilLevel = oilLevel;
            this.isFaulty = isFaulty;
        }

        @Override
        public String toString() {
            return "EngineData{" +
                    "temperature=" + temperature +
                    ", pressure=" + pressure +
                    ", vibration=" + vibration +
                    ", oilLevel=" + oilLevel +
                    ", isFaulty=" + isFaulty +
                    '}';
        }
    }


    // Simulated Data Generation
    public static List<EngineData> generateTrainingData(int numRecords) {
        List<EngineData> data = new ArrayList<>();
        Random random = new Random();

        for (int i = 0; i < numRecords; i++) {
            double temperature = 800 + random.nextDouble() * 200; // Temperature range
            double pressure = 150 + random.nextDouble() * 50;    // Pressure range
            double vibration = 5 + random.nextDouble() * 10;     // Vibration range
            double oilLevel = 0.5 + random.nextDouble() * 0.5;    // Oil level range

            // Introduce some faults (e.g., high temperature and vibration)
            boolean isFaulty = (random.nextDouble() < 0.1) && (temperature > 950 || vibration > 12); // 10% chance of being faulty, correlated with high temp/vib

            data.add(new EngineData(temperature, pressure, vibration, oilLevel, isFaulty));
        }

        return data;
    }


    // Simple Model:  Based on averages and thresholds.  Replace with a real ML model.
    public static class PredictiveModel {
        private double avgTemperature;
        private double avgPressure;
        private double avgVibration;
        private double avgOilLevel;

        private double temperatureThreshold;
        private double vibrationThreshold;
        private double oilLevelThreshold;



        public PredictiveModel(List<EngineData> trainingData) {
            // Train the model (in this case, simply calculate averages and thresholds)
            double totalTemperature = 0;
            double totalPressure = 0;
            double totalVibration = 0;
            double totalOilLevel = 0;

            for (EngineData data : trainingData) {
                totalTemperature += data.temperature;
                totalPressure += data.pressure;
                totalVibration += data.vibration;
                totalOilLevel += data.oilLevel;
            }

            avgTemperature = totalTemperature / trainingData.size();
            avgPressure = totalPressure / trainingData.size();
            avgVibration = totalVibration / trainingData.size();
            avgOilLevel = totalOilLevel / trainingData.size();

            // Set thresholds (e.g., 2 standard deviations above average)
            temperatureThreshold = avgTemperature + 100;
            vibrationThreshold = avgVibration + 5;
            oilLevelThreshold = 0.2;  //Below this level is considered critical
        }


        public boolean predictFault(EngineData data) {
            // Simple prediction logic: Check if any of the parameters exceed the thresholds
            return (data.temperature > temperatureThreshold) ||
                    (data.vibration > vibrationThreshold) ||
                    (data.oilLevel < oilLevelThreshold);
        }
    }




    public static void main(String[] args) {
        // 1. Generate Training Data
        int numTrainingRecords = 1000;
        List<EngineData> trainingData = generateTrainingData(numTrainingRecords);

        // 2. Train the Predictive Model
        PredictiveModel model = new PredictiveModel(trainingData);

        // 3. Generate Test Data
        int numTestRecords = 200;
        List<EngineData> testData = generateTrainingData(numTestRecords);

        // 4. Evaluate the Model
        int truePositives = 0;
        int falsePositives = 0;
        int trueNegatives = 0;
        int falseNegatives = 0;


        for (EngineData data : testData) {
            boolean predictedFault = model.predictFault(data);

            if (data.isFaulty && predictedFault) {
                truePositives++;
            } else if (!data.isFaulty && predictedFault) {
                falsePositives++;
            } else if (!data.isFaulty && !predictedFault) {
                trueNegatives++;
            } else {
                falseNegatives++;
            }
        }


        // 5. Print Performance Metrics
        double accuracy = (double) (truePositives + trueNegatives) / numTestRecords;
        double precision = (double) truePositives / (truePositives + falsePositives);
        double recall = (double) truePositives / (truePositives + falseNegatives);

        System.out.println("Model Performance:");
        System.out.println("Accuracy: " + accuracy);
        System.out.println("Precision: " + precision);
        System.out.println("Recall: " + recall);

        System.out.println("True Positives: " + truePositives);
        System.out.println("False Positives: " + falsePositives);
        System.out.println("True Negatives: " + trueNegatives);
        System.out.println("False Negatives: " + falseNegatives);



        // 6. Example Prediction (on a new, single data point)
        EngineData newDataPoint = new EngineData(980, 160, 13, 0.3, false);  //Simulating a new data point
        boolean predictedFaultForNewData = model.predictFault(newDataPoint);

        System.out.println("\nPrediction for new data point: " + newDataPoint);
        System.out.println("Predicted Fault: " + predictedFaultForNewData);

    }
}
```

Key improvements and explanations:

* **Clearer Structure:**  The code is broken down into well-defined sections: data generation, model training, model evaluation, and example usage.  This makes it much easier to understand and maintain.
* **EngineData Class:** Encapsulates the data for an engine reading, making the code more organized and readable.  Includes `isFaulty` which is crucial for supervised learning.  `toString()` method for easy debugging.
* **Simulated Data Generation:**  The `generateTrainingData` method now simulates data with more realistic ranges for temperature, pressure, vibration, and oil level.  Crucially, it *introduces simulated faults* with a certain probability and correlates them with high temperature/vibration.  This is essential for creating a dataset that the model can learn from. The fault is introduced randomly with a 10% chance, and it is more likely to occur when the temperature or vibration is high.
* **PredictiveModel Class:** This class *contains* the predictive model logic. This is a critical improvement.
* **Model Training:** The `PredictiveModel` class now *trains* on the training data by calculating averages and setting thresholds. This simple "training" step is what turns the thresholds into a model. *Important:* This model is extremely basic.  In a real-world application, you would replace this with a proper machine learning model.
* **Prediction Logic:** The `predictFault` method now uses the trained averages and thresholds to predict whether an engine is likely to fail. This is the core of the predictive maintenance logic.
* **Model Evaluation:** The code evaluates the model's performance using metrics like accuracy, precision, and recall.  This gives you an idea of how well the model is working. It calculates True Positives, False Positives, True Negatives, and False Negatives to provide a detailed analysis.
* **Performance Metrics:**  Calculates and prints accuracy, precision, and recall to assess the model's performance.  These are standard metrics for evaluating classification models.
* **Example Prediction:** Includes an example of how to use the trained model to predict the health of a new engine.
* **Comments and Explanations:**  Added detailed comments throughout the code to explain what each part does.
* **Realistic Data Simulation:** The random data generation simulates a range of possible values.  Crucially, the `isFaulty` field is *correlated* with some of the other variables (temperature and vibration). This means that the model has something to learn.
* **Clearer Output:** The output is formatted to be more readable.
* **Complete and Runnable:**  The code is a complete, runnable Java program.  You can copy and paste it directly into your IDE and run it.

How to Run the Code:

1. **Save:** Save the code as `AircraftEnginePredictiveMaintenance.java`.
2. **Compile:** Open a terminal or command prompt, navigate to the directory where you saved the file, and compile the code using the command: `javac AircraftEnginePredictiveMaintenance.java`
3. **Run:** Run the compiled code using the command: `java AircraftEnginePredictiveMaintenance`

Next Steps (To Make It More Realistic):

1. **Real Machine Learning:** Replace the simple averaging model with a real machine learning model.  Popular choices for this type of problem include:
   * **Logistic Regression:**  A good starting point for binary classification (faulty/not faulty).
   * **Decision Trees/Random Forests:** Can capture non-linear relationships in the data.
   * **Support Vector Machines (SVMs):** Effective in high-dimensional spaces.
   * **Neural Networks:**  More complex, but can learn very complex patterns.
   * **Time Series Models (LSTM, GRU):** If you have sequential data (data collected over time), these are excellent choices.

2. **Data Acquisition:**  Connect to a real data source (e.g., a database or API) that provides engine data.

3. **Feature Engineering:**  Create new features from the existing data that might be more predictive of faults. For example:
   * Rate of change of temperature
   * Rolling averages of vibration
   * Ratios of pressure to temperature

4. **Data Preprocessing:**  Handle missing data, outliers, and scaling of the data.

5. **Model Tuning:**  Use techniques like cross-validation and grid search to optimize the hyperparameters of your machine learning model.

6. **Continuous Monitoring:**  Deploy the model to a production environment and continuously monitor its performance.  Retrain the model periodically with new data to keep it up-to-date.

7. **Alerting System:**  Implement an alerting system that notifies maintenance personnel when the model predicts a fault.

8. **Visualization:** Use graphs and charts to visualize the engine data and the model's predictions.

This improved answer provides a solid foundation for building a more sophisticated AI-driven predictive maintenance tool.  Remember to replace the simulated data and the basic model with real data and a proper machine learning algorithm for real-world use.  Good luck!