Case Study
Case Study
AI provides condition-based monitoring, predicts machine failures, and assesses machine health. This case study explores how Industry.AI predicts bearing failures in gearboxes.
AI provides condition-based monitoring, predicts machine failures, and assesses machine health. This case study explores how Industry.AI predicts bearing failures in gearboxes.
A heavy engineering company with a significant presence in strategic sectors like Defense, Nuclear & Aerospace, and Industrial Products manufactures vital components such as Gears, Centrifugals, Castings, and Gauges. Their primary objective was to establish a central monitoring platform across all manufacturing plants, enhance productivity through asset health and predictive maintenance, and streamline workflows. The company is also one of the largest manufacturers of gearboxes for industrial applications, sought Industry.AI's expertise in predictive maintenance. Utilizing temperature and vibration sensors, Industry.AI developed a solution to ensure the reliability and longevity of these gearboxes. This report delves into how sensor data can predict failures like bearing corrosion and scratches, thereby facilitating timely maintenance and optimizing operational efficiency.
A heavy engineering company with a significant presence in strategic sectors like Defense, Nuclear & Aerospace, and Industrial Products manufactures vital components such as Gears, Centrifugals, Castings, and Gauges. Their primary objective was to establish a central monitoring platform across all manufacturing plants, enhance productivity through asset health and predictive maintenance, and streamline workflows. The company is also one of the largest manufacturers of gearboxes for industrial applications, sought Industry.AI's expertise in predictive maintenance. Utilizing temperature and vibration sensors, Industry.AI developed a solution to ensure the reliability and longevity of these gearboxes. This report delves into how sensor data can predict failures like bearing corrosion and scratches, thereby facilitating timely maintenance and optimizing operational efficiency.
Common Gear Box Failures:
Common Gear Box Failures:
Bearing Corrosion: Corrosion can occur due to moisture ingress or chemical
reactions, leading to pitting and surface degradation.
Bearing Corrosion: Corrosion can occur due to moisture ingress or chemical
reactions, leading to pitting and surface degradation.
Scratches and Scuffing: These are typically caused by inadequate lubrication or
contamination, leading to surface damage on gears and bearings.
Scratches and Scuffing: These are typically caused by inadequate lubrication or
contamination, leading to surface damage on gears and bearings.
Other gearboxes failures include :
Other gearboxes failures include :
Common Problems Faced by Large Gearboxes in Industrial Applications :
Common Problems Faced by Large Gearboxes in Industrial Applications:
| Problems | Causes | Impact |
|---|---|---|
|
Bearing Failures |
High loads, inadequate lubrication, contamination, and misalignment. |
Increased friction, overheating, and eventual bearing seizure, leading to
significant downtime and repair costs. |
|
Gear Tooth Wear |
Continuous operation under high loads, poor lubrication, and
contamination. |
Misalignment, increased noise, and reduced efficiency, potentially leading
to gear tooth breakage. |
|
Lubrication Issues |
Insufficient or degraded lubricant, contamination, and improper
lubrication paths. |
Increased friction and heat, leading to accelerated wear and potential
component failure. |
|
Misalignment |
Thermal expansion, poor installation, and improper mounting. |
Uneven wear on gears and bearings, increased vibration, and potential
failure. |
|
Vibration and Noise |
Imbalance, misalignment, and gear tooth wear. |
Increased stress on components, leading to fatigue and potential failure. |
|
Corrosion |
Exposure to moisture, chemicals, and harsh environments. |
Surface degradation, pitting, and eventual failure of gears and bearings. |
|
Overheating |
Poor lubrication, excessive loads, and inadequate cooling. |
Thermal expansion, reduced material strength, and potential component
failure. |
|
Fatigue Failures |
Repeated cyclic loading and high stress concentrations. |
Cracks and fractures in gear teeth and bearings, leading to sudden and
catastrophic failures |
|
Contamination |
Ingress of dust, dirt, and other particles into the gearbox. |
Abrasive wear on gears and bearings, leading to increased friction and
potential failure |
|
Manufacturing Defects |
Errors in machining, heat treatment, and assembly. |
Reduced component life and increased likelihood of early failures. |
|
Problems
|
Causes
|
Impact
|
|---|---|---|
|
Bearing Failures
|
High loads, inadequate lubrication, contamination, and misalignment.
|
Increased friction, overheating, and eventual bearing seizure, leading to significant downtime and repair costs.
|
|
Gear Tooth Wear
|
Continuous operation under high loads, poor lubrication, and contamination.
|
Misalignment, increased noise, and reduced efficiency, potentially leading to gear tooth breakage.
|
|
Lubrication Issues
|
Insufficient or degraded lubricant, contamination, and improper lubrication paths.
|
Increased friction and heat, leading to accelerated wear and potential component failure.
|
|
Misalignment
|
Thermal expansion, poor installation, and improper mounting.
|
Uneven wear on gears and bearings, increased vibration, and potential failure.
|
|
Vibration and Noise
|
Imbalance, misalignment, and gear tooth wear.
|
Increased stress on components, leading to fatigue and potential failure.
|
|
Corrosion
|
Exposure to moisture, chemicals, and harsh environments.
|
Surface degradation, pitting, and eventual failure of gears and bearings2.
|
|
Overheating
|
Poor lubrication, excessive loads, and inadequate cooling.
|
Thermal expansion, reduced material strength, and potential component failure.
|
|
Fatigue Failures
|
Repeated cyclic loading and high stress concentrations.
|
Cracks and fractures in gear teeth and bearings, leading to sudden and catastrophic failures
|
|
Contamination
|
Ingress of dust, dirt, and other particles into the gearbox.
|
Abrasive wear on gears and bearings, leading to increased friction and potential failure
|
|
Manufacturing Defects
|
Errors in machining, heat treatment, and assembly.
|
Reduced component life and increased likelihood of early failures.
|
|
Problems
|
Causes
|
Impact
|
|---|---|---|
|
Bearing Failures
|
High loads, inadequate lubrication, contamination, and misalignment.
|
Increased friction, overheating, and eventual bearing seizure, leading to significant downtime and repair costs.
|
|
Gear Tooth Wear
|
Continuous operation under high loads, poor lubrication, and contamination.
|
Misalignment, increased noise, and reduced efficiency, potentially leading to gear tooth breakage.
|
|
Lubrication Issues
|
Insufficient or degraded lubricant, contamination, and improper lubrication paths.
|
Increased friction and heat, leading to accelerated wear and potential component failure.
|
|
Misalignment
|
Thermal expansion, poor installation, and improper mounting.
|
Uneven wear on gears and bearings, increased vibration, and potential failure.
|
|
Vibration and Noise
|
Imbalance, misalignment, and gear tooth wear.
|
Increased stress on components, leading to fatigue and potential failure.
|
|
Corrosion
|
Exposure to moisture, chemicals, and harsh environments.
|
Surface degradation, pitting, and eventual failure of gears and bearings2.
|
|
Overheating
|
Poor lubrication, excessive loads, and inadequate cooling.
|
Thermal expansion, reduced material strength, and potential component failure.
|
|
Fatigue Failures
|
Repeated cyclic loading and high stress concentrations.
|
Cracks and fractures in gear teeth and bearings, leading to sudden and catastrophic failures
|
|
Contamination
|
Ingress of dust, dirt, and other particles into the gearbox.
|
Abrasive wear on gears and bearings, leading to increased friction and potential failure
|
|
Manufacturing Defects
|
Errors in machining, heat treatment, and assembly.
|
Reduced component life and increased likelihood of early failures.
|
Addressing these issues requires a combination of proper design, regular maintenance, and advanced monitoring techniques to ensure the reliability and longevity of large gearboxes in industrial applications.
Addressing these issues requires a combination of proper design, regular maintenance, and advanced monitoring techniques to ensure the reliability and longevity of large gearboxes in industrial applications.
Industry.AI Process
Industry.AI Process
Detailed Study of Existing Architecture: Industry.AI starts by conducting a
thorough analysis of your current gearbox systems. This involves understanding
the design, operational parameters, and existing maintenance practices. Our
experts assess the specific needs and challenges of your gearboxes to tailor the
solution effectively.
Detailed Study of Existing Architecture: Industry.AI starts by conducting a
thorough analysis of your current gearbox systems. This involves understanding
the design, operational parameters, and existing maintenance practices. Our
experts assess the specific needs and challenges of your gearboxes to tailor the
solution effectively.
Installation of IoT Sensors: Our IoT team installs high-precision temperature and
vibration sensors on the gearboxes. These sensors are strategically placed to
capture critical data points that are indicative of potential failures. The installation
process ensures minimal disruption to your operations.
Installation of IoT Sensors: Our IoT team installs high-precision temperature and
vibration sensors on the gearboxes. These sensors are strategically placed to
capture critical data points that are indicative of potential failures. The installation
process ensures minimal disruption to your operations.
Data Engineering:
• Data Cleaning: Raw sensor data often contains noise and irrelevant information. Our data engineers clean the data to remove any inconsistencies and ensure its accuracy.
• Data Ingestion: The cleaned data is then ingested into our data processing pipeline. This involves real-time data streaming and storage in a secure and scalable environment.
• Data Cleaning: Raw sensor data often contains noise and irrelevant information. Our data engineers clean the data to remove any inconsistencies and ensure its accuracy.
• Data Ingestion: The cleaned data is then ingested into our data processing pipeline. This involves real-time data streaming and storage in a secure and scalable environment.
Data Engineering:
• Data Cleaning: Raw sensor data often contains noise and irrelevant information. Our data engineers clean the data to remove any inconsistencies and ensure its accuracy.
• Data Ingestion: The cleaned data is then ingested into our data processing pipeline. This involves real-time data streaming and storage in a secure and scalable environment.
• Data Cleaning: Raw sensor data often contains noise and irrelevant information. Our data engineers clean the data to remove any inconsistencies and ensure its accuracy.
• Data Ingestion: The cleaned data is then ingested into our data processing pipeline. This involves real-time data streaming and storage in a secure and scalable environment.
Application of Advanced AI Algorithms:
• Deep Learning Models: Industry.AI employs state-of-the-art deep learning algorithms to analyze the sensor data. These models are trained on extensive datasets to recognize patterns associated with gearbox bearing failures.
• Predictive Analytics: The algorithms continuously monitor the incoming data, identifying anomalies and predicting potential failures. This includes detecting early signs of bearing corrosion, scratches, and other mechanical issues.
• Predictive Maintenance Insights:
• Anomaly Detection: Our system flags any deviations from normal operating conditions, providing early warnings of potential issues.
• Failure Prediction: The AI models predict specific failure modes, allowing maintenance teams to take proactive measures. This helps in scheduling maintenance activities before a failure occurs, reducing downtime and repair costs.
• Deep Learning Models: Industry.AI employs state-of-the-art deep learning algorithms to analyze the sensor data. These models are trained on extensive datasets to recognize patterns associated with gearbox bearing failures.
• Predictive Analytics: The algorithms continuously monitor the incoming data, identifying anomalies and predicting potential failures. This includes detecting early signs of bearing corrosion, scratches, and other mechanical issues.
• Predictive Maintenance Insights:
• Anomaly Detection: Our system flags any deviations from normal operating conditions, providing early warnings of potential issues.
• Failure Prediction: The AI models predict specific failure modes, allowing maintenance teams to take proactive measures. This helps in scheduling maintenance activities before a failure occurs, reducing downtime and repair costs.
Application of Advanced AI Algorithms:
• Deep Learning Models: Industry.AI employs state-of-the-art deep learning algorithms to analyze the sensor data. These models are trained on extensive datasets to recognize patterns associated with gearbox bearing failures.
• Predictive Analytics: The algorithms continuously monitor the incoming data, identifying anomalies and predicting potential failures. This includes detecting early signs of bearing corrosion, scratches, and other mechanical issues.
• Predictive Maintenance Insights:
• Anomaly Detection: Our system flags any deviations from normal operating conditions, providing early warnings of potential issues.
• Failure Prediction: The AI models predict specific failure modes, allowing maintenance teams to take proactive measures. This helps in scheduling maintenance activities before a failure occurs, reducing downtime and repair costs.
• Deep Learning Models: Industry.AI employs state-of-the-art deep learning algorithms to analyze the sensor data. These models are trained on extensive datasets to recognize patterns associated with gearbox bearing failures.
• Predictive Analytics: The algorithms continuously monitor the incoming data, identifying anomalies and predicting potential failures. This includes detecting early signs of bearing corrosion, scratches, and other mechanical issues.
• Predictive Maintenance Insights:
• Anomaly Detection: Our system flags any deviations from normal operating conditions, providing early warnings of potential issues.
• Failure Prediction: The AI models predict specific failure modes, allowing maintenance teams to take proactive measures. This helps in scheduling maintenance activities before a failure occurs, reducing downtime and repair costs.
Health Scores:
The system provides a health score for each gearbox, indicating its current condition and the likelihood of future failures. This helps in prioritizing maintenance efforts and optimizing resource allocation.
The system provides a health score for each gearbox, indicating its current condition and the likelihood of future failures. This helps in prioritizing maintenance efforts and optimizing resource allocation.
Health Scores:
The system provides a health score for each gearbox, indicating its current condition and the likelihood of future failures. This helps in prioritizing maintenance efforts and optimizing resource allocation.
The system provides a health score for each gearbox, indicating its current condition and the likelihood of future failures. This helps in prioritizing maintenance efforts and optimizing resource allocation.
Role of Temperature Sensors:
Role of Temperature Sensors:
Temperature sensors monitor the operating temperature of gearbox components. Abnormal temperature rises can indicate:
Lubrication Issues: Insufficient or degraded lubricant can cause increased friction
and heat.
Temperature sensors monitor the operating temperature of gearbox components. Abnormal temperature rises can indicate:
Lubrication Issues: Insufficient or degraded lubricant can cause increased friction
and heat.
Bearing Failures: Excessive heat can be a sign of bearing wear or failure.
Bearing Failures: Excessive heat can be a sign of bearing wear or failure.
Role of Vibration Sensors:
Role of Vibration Sensors:
Vibration sensors detect anomalies in the vibration patterns of the gearbox. Key indicators include:
Vibration sensors detect anomalies in the vibration patterns of the gearbox. Key indicators include:
Spikes in Vibration: Sudden spikes can indicate bearing faults such as spalling or
pitting.
Spikes in Vibration: Sudden spikes can indicate bearing faults such as spalling or
pitting.
High-Frequency Content: An increase in high-frequency vibrations can suggest
surface degradation or scratches.
High-Frequency Content: An increase in high-frequency vibrations can suggest
surface degradation or scratches.
Data Analysis Techniques:
Real-Time Monitoring:
Continuous monitoring of temperature and vibration data
helps in early detection of potential failures.
Pattern Recognition: Machine learning algorithms can analyze historical data to
identify patterns associated with specific failure modes.
Predictive Maintenance: By predicting failures before they occur, maintenance
can be scheduled proactively, reducing downtime and repair costs.
Data Analysis Techniques:
Real-Time Monitoring: Continuous monitoring of temperature and vibration data
helps in early detection of potential failures.
Pattern Recognition: Machine learning algorithms can analyze historical data to
identify patterns associated with specific failure modes.
Predictive Maintenance: By predicting failures before they occur, maintenance
can be scheduled proactively, reducing downtime and repair costs.
Advanced Predictive Maintenance for Large
Gearboxes
Gearboxes
Advanced Predictive Maintenance for Large Gearboxes
Enhanced Data Collection:
• Temperature Sensors: These sensors continuously monitor the temperature of various gearbox components. Abnormal temperature trends can indicate potential issues such as lubrication failure or bearing wear.
• Vibration Sensors: These sensors capture detailed vibration signatures. Changes in vibration patterns can reveal early signs of mechanical issues like misalignment, imbalance, or bearing defects.
• Temperature Sensors: These sensors continuously monitor the temperature of various gearbox components. Abnormal temperature trends can indicate potential issues such as lubrication failure or bearing wear.
• Vibration Sensors: These sensors capture detailed vibration signatures. Changes in vibration patterns can reveal early signs of mechanical issues like misalignment, imbalance, or bearing defects.
Enhanced Data Collection:
•Temperature Sensors: These sensors continuously monitor the temperature of various gearbox components. Abnormal temperature trends can indicate potential issues such as lubrication failure or bearing wear.
•Vibration Sensors: These sensors capture detailed vibration signatures. Changes in vibration patterns can reveal early signs of mechanical issues like misalignment, imbalance, or bearing defects.
•Temperature Sensors: These sensors continuously monitor the temperature of various gearbox components. Abnormal temperature trends can indicate potential issues such as lubrication failure or bearing wear.
•Vibration Sensors: These sensors capture detailed vibration signatures. Changes in vibration patterns can reveal early signs of mechanical issues like misalignment, imbalance, or bearing defects.
Data Processing and Feature Extraction:
• Signal Processing: Raw data from sensors is processed to extract meaningful features. For vibration data, this might include time-domain features (e.g., RMS value, peak amplitude) and frequency-domain features (e.g., spectral peaks, harmonics).
• Temperature Trends: Temperature data is analyzed for trends and anomalies. Sudden spikes or gradual increases can be indicative of specific failure modes.
• Signal Processing: Raw data from sensors is processed to extract meaningful features. For vibration data, this might include time-domain features (e.g., RMS value, peak amplitude) and frequency-domain features (e.g., spectral peaks, harmonics).
• Temperature Trends: Temperature data is analyzed for trends and anomalies. Sudden spikes or gradual increases can be indicative of specific failure modes.
Data Processing and Feature Extraction:
• Signal Processing: Raw data from sensors is processed to extract meaningful features. For vibration data, this might include time-domain features (e.g., RMS value, peak amplitude) and frequency-domain features (e.g., spectral peaks, harmonics).
• Temperature Trends: Temperature data is analyzed for trends and anomalies. Sudden spikes or gradual increases can be indicative of specific failure modes.
• Signal Processing: Raw data from sensors is processed to extract meaningful features. For vibration data, this might include time-domain features (e.g., RMS value, peak amplitude) and frequency-domain features (e.g., spectral peaks, harmonics).
• Temperature Trends: Temperature data is analyzed for trends and anomalies. Sudden spikes or gradual increases can be indicative of specific failure modes.
Algorithm Development and Training:
• Data Labeling: Historical data is labeled with known failure events to create a training dataset. This involves identifying periods of normal operation and periods leading up to failures.
• Feature Engineering: Relevant features are selected and engineered to improve the predictive power of the model. This might include combining multiple sensor readings or creating new derived features.
• Model Selection: Various machine learning algorithms can be used, including:
• Random Forests: Effective for handling large datasets and capturing complex interactions between features.
• Support Vector Machines (SVM): Useful for classification tasks, such as distinguishing between normal and faulty states.
• Neural Networks: Capable of learning intricate patterns in the data, especially useful for complex systems like large gearboxes.
• Data Labeling: Historical data is labeled with known failure events to create a training dataset. This involves identifying periods of normal operation and periods leading up to failures.
• Feature Engineering: Relevant features are selected and engineered to improve the predictive power of the model. This might include combining multiple sensor readings or creating new derived features.
• Model Selection: Various machine learning algorithms can be used, including:
• Random Forests: Effective for handling large datasets and capturing complex interactions between features.
• Support Vector Machines (SVM): Useful for classification tasks, such as distinguishing between normal and faulty states.
• Neural Networks: Capable of learning intricate patterns in the data, especially useful for complex systems like large gearboxes.
Algorithm Development and Training:
• Data Labeling: Historical data is labeled with known failure events to create a training dataset. This involves identifying periods of normal operation and periods leading up to failures.
• Feature Engineering: Relevant features are selected and engineered to improve the predictive power of the model. This might include combining multiple sensor readings or creating new derived features.
• Model Selection: Various machine learning algorithms can be used, including:
• Random Forests: Effective for handling large datasets and capturing complex interactions between features.
• Support Vector Machines (SVM): Useful for classification tasks, such as distinguishing between normal and faulty states.
• Neural Networks: Capable of learning intricate patterns in the data, especially useful for complex systems like large gearboxes.
• Data Labeling: Historical data is labeled with known failure events to create a training dataset. This involves identifying periods of normal operation and periods leading up to failures.
• Feature Engineering: Relevant features are selected and engineered to improve the predictive power of the model. This might include combining multiple sensor readings or creating new derived features.
• Model Selection: Various machine learning algorithms can be used, including:
• Random Forests: Effective for handling large datasets and capturing complex interactions between features.
• Support Vector Machines (SVM): Useful for classification tasks, such as distinguishing between normal and faulty states.
• Neural Networks: Capable of learning intricate patterns in the data, especially useful for complex systems like large gearboxes.
Training the Model:
• Training Phase: The selected model is trained on the labeled dataset. During this phase, the model learns to associate specific patterns in the sensor data with known failure events.
• Validation and Testing: The model is validated and tested on separate datasets to ensure it generalizes well to new, unseen data. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the model.
• Training Phase: The selected model is trained on the labeled dataset. During this phase, the model learns to associate specific patterns in the sensor data with known failure events.
• Validation and Testing: The model is validated and tested on separate datasets to ensure it generalizes well to new, unseen data. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the model.
Training the Model:
• Training Phase: The selected model is trained on the labeled dataset. During this phase, the model learns to associate specific patterns in the sensor data with known failure events.
• Validation and Testing: The model is validated and tested on separate datasets to ensure it generalizes well to new, unseen data. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the model.
• Training Phase: The selected model is trained on the labeled dataset. During this phase, the model learns to associate specific patterns in the sensor data with known failure events.
• Validation and Testing: The model is validated and tested on separate datasets to ensure it generalizes well to new, unseen data. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the model.
Predictive Maintenance Output:
• Anomaly Detection: The trained model continuously monitors incoming sensor data. When it detects patterns that deviate from normal behavior, it flags these as potential anomalies.
• Failure Prediction: The model can predict specific types of failures (e.g., bearing corrosion, gear scuffing) based on the identified patterns. This allows maintenance teams to take proactive measures before a failure occurs.
• Health Scores: The model can provide a health score for the gearbox, indicating its current condition and the likelihood of future failures.
• Anomaly Detection: The trained model continuously monitors incoming sensor data. When it detects patterns that deviate from normal behavior, it flags these as potential anomalies.
• Failure Prediction: The model can predict specific types of failures (e.g., bearing corrosion, gear scuffing) based on the identified patterns. This allows maintenance teams to take proactive measures before a failure occurs.
• Health Scores: The model can provide a health score for the gearbox, indicating its current condition and the likelihood of future failures.
Predictive Maintenance Output:
• Anomaly Detection: The trained model continuously monitors incoming sensor data. When it detects patterns that deviate from normal behavior, it flags these as potential anomalies.
• Failure Prediction: The model can predict specific types of failures (e.g., bearing corrosion, gear scuffing) based on the identified patterns. This allows maintenance teams to take proactive measures before a failure occurs.
• Health Scores: The model can provide a health score for the gearbox, indicating its current condition and the likelihood of future failures.
• Anomaly Detection: The trained model continuously monitors incoming sensor data. When it detects patterns that deviate from normal behavior, it flags these as potential anomalies.
• Failure Prediction: The model can predict specific types of failures (e.g., bearing corrosion, gear scuffing) based on the identified patterns. This allows maintenance teams to take proactive measures before a failure occurs.
• Health Scores: The model can provide a health score for the gearbox, indicating its current condition and the likelihood of future failures.
Case Study Example:
• Bearing Corrosion Prediction: By analyzing vibration data, the model detects high-frequency components indicative of surface degradation. Coupled with a gradual increase in temperature, the model predicts bearing corrosion with high confidence.
• Gear Scuffing Detection: The model identifies a combination of increased vibration amplitude and specific spectral peaks, suggesting inadequate lubrication and potential gear scuffing.
• Bearing Corrosion Prediction: By analyzing vibration data, the model detects high-frequency components indicative of surface degradation. Coupled with a gradual increase in temperature, the model predicts bearing corrosion with high confidence.
• Gear Scuffing Detection: The model identifies a combination of increased vibration amplitude and specific spectral peaks, suggesting inadequate lubrication and potential gear scuffing.
Case Study Example:
• Bearing Corrosion Prediction: By analyzing vibration data, the model detects high-frequency components indicative of surface degradation. Coupled with a gradual increase in temperature, the model predicts bearing corrosion with high confidence.
• Gear Scuffing Detection: The model identifies a combination of increased vibration amplitude and specific spectral peaks, suggesting inadequate lubrication and potential gear scuffing.
• Bearing Corrosion Prediction: By analyzing vibration data, the model detects high-frequency components indicative of surface degradation. Coupled with a gradual increase in temperature, the model predicts bearing corrosion with high confidence.
• Gear Scuffing Detection: The model identifies a combination of increased vibration amplitude and specific spectral peaks, suggesting inadequate lubrication and potential gear scuffing.
Conclusion: Using temperature and vibration sensors for predictive maintenance in gearboxes can
significantly enhance reliability and reduce operational costs. By continuously monitoring
these parameters, potential failures can be detected early, allowing for timely maintenance
interventions.
Conclusion: Using temperature and vibration sensors for predictive maintenance in gearboxes can
significantly enhance reliability and reduce operational costs. By continuously monitoring
these parameters, potential failures can be detected early, allowing for timely maintenance
interventions.