Practical ML Examples for maintainers interested in tech – Part II

As we transition into Part 2 of our exploration of machine learning (ML) in maintenance, we will build upon the foundational concepts introduced in Part 1. In the previous section, we examined how traditional maintenance systems have evolved into sophisticated ML models, enhancing operational efficiency and enabling predictive capabilities. We discussed the importance of data collection, the advantages of ML over basic systems, and highlighted various algorithms that can be employed to predict equipment failures effectively. For a deeper understanding, you can revisit the first part here. In this next segment, Practical ML Examples for maintainers interested in tech – Part II, we will provide actionable insights into developing predictive models tailored for maintenance applications. We will explore:

• Practical ML Examples on Building ML Models for Maintenance
• Case Studies: Machine Learning Success Stories in Maintenance
  • Case Study 1: Predictive Maintenance in Manufacturing
  • Case Study 2: Predictive Maintenance in Aviation
  • Case Study 3: Predictive Maintenance in the Energy Sector
  • Case Study 4: Predictive Maintenance in Transportation

Stay tuned as we delve into practical strategies and real-world applications that demonstrate the power of machine learning in optimizing maintenance practices across various industries.

5. Practical ML Example: Building ML Models for Maintenance

For maintenance practitioners looking to dive into machine learning, platforms like Google Colab offer a free, accessible way to experiment with ML models. Here’s a step-by-step guide to get started with an ML example:

1. Data Collection: Gather historical maintenance data, including equipment parameters, maintenance actions, and failure events.
2. Data Preprocessing: Clean the data, handle missing values, and normalize or standardize numerical features.
3. Feature Selection: Identify the most relevant features that contribute to equipment health and failure prediction.
4. Model Selection: Choose an appropriate ML algorithm based on your problem (e.g., regression for continuous predictions, classification for failure/no-failure predictions).
5. Model Training: Split your data into training and testing sets, then train your chosen model on the training data.
6. Model Evaluation: Assess the model’s performance using appropriate metrics (e.g., accuracy, precision, recall for classification; mean squared error for regression).
7. Model Deployment: Integrate the trained model into your maintenance workflow, either through a custom application or by exporting predictions to your existing maintenance system.

Example: Predicting Equipment Failure

Let’s walk through a practical example of how to build a simple machine learning model for predicting equipment failure. We’ll use a random forest classifier for this example.

First, let’s assume we have collected data on various pieces of equipment over time. This data is stored in a CSV file named ‘equipment_data.csv’. The file contains the following columns:

• equipment_id: A unique identifier for each piece of equipment
• age: The age of the equipment in years
• temperature: The average operating temperature
• vibration: The average vibration level
• pressure: The average operating pressure
• failure: Whether the equipment failed (1) or not (0) in the next month

Here’s how we might approach building a model with this data:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

# Load the data
data = pd.read_csv('equipment_data.csv')

# Prepare features (X) and target variable (y)
X = data[['age', 'temperature', 'vibration', 'pressure']]
y = data['failure']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")
print("\nClassification Report:")
print(classification_report(y_test, y_pred))

How does this ML Example work?

In this example, we’re using historical data to train a random forest classifier to predict whether a piece of equipment is likely to fail in the next month. The model takes into account the equipment’s age, average operating temperature, vibration level, and pressure.

The data collection process for this model would involve setting up sensors on the equipment to continuously monitor temperature, vibration, and pressure. The age of the equipment would be known from maintenance records. The ‘failure’ data would come from historical records of when each piece of equipment failed.

After training, the model can be used to predict the likelihood of failure for new, unseen equipment data. This allows maintenance teams to prioritize their efforts on equipment that’s most likely to fail, potentially preventing unexpected downtime and reducing maintenance costs.

It’s important to note that this is a simplified example. In practice, you might need to handle more complex data, deal with imbalanced datasets (where failures are rare events), and potentially use more sophisticated models or ensemble methods to achieve the best results.

6. Case Studies: Machine Learning Success Stories in Maintenance

Let’s explore some real-world applications of machine learning in maintenance across various industries:

Case Study 1: Predictive Maintenance in Manufacturing

Frito-Lay, a subsidiary of PepsiCo, implemented a machine learning-based predictive maintenance system in their manufacturing plants. By analyzing sensor data from their production lines, they were able to predict potential failures up to 36 hours in advance. Also, They have a patent for a sensor that detects how crunchy is the chips by training it to the sound of potato chips at different transitions and belts. System was trained on the sounds of good products and low quality ones and take decision on accepting and rejecting the product. This led to a significant reduction in unplanned downtime and increased overall equipment effectiveness (OEE).

Reference: How Frito-Lay Is Using ML To Revolutionize The Factory Floor

Case Study 2: Predictive Maintenance in Aviation

Delta Air Lines has been using machine learning algorithms to predict aircraft component failures before they occur. By analyzing data from thousands of sensors across their fleet, Delta has been able to reduce maintenance-related flight cancellations and delays significantly.

Delta partnered with an AI technology company to implement a predictive maintenance system that would address three main aspects:

• Data Collection – Adopting sensors and IoT devices that, once installed on Delta’s aircraft, allow the airlines to collect vast amounts of real-time data, including engine performance, temperature, pressure, and component wear;
• Data Analytics – AI algorithms were developed to analyze the data continuously. These algorithms could detect anomalies and patterns indicative of equipment degradation or potential failures. Machine learning models were trained to predict when specific components would require maintenance; and
• Predictive Alerts – The AI system generated predictive alerts for maintenance crews when it detected issues that needed attention. These alerts were based on the actual condition of aircraft components rather than scheduled maintenance intervals.

Reference: How Delta is using AI to improve maintenance & reliability

Case Study 3: Predictive Maintenance in Energy Sector

Included in the Digital Twin models are all necessary aspects of the physical asset or larger system including thermal, mechanical, electrical, chemical, fluid dynamic, material, lifing, economic and statistical. These models also accurately represent the plant or fleet under a large number of variations related to operation — fuel mix, ambient temperature, air quality, moisture, load, weather forecast models, and market pricing. Using these digital twin models and state-of-the-art techniques of optimization, control, and forecasting, applications can more accurately predict outcomes along different axes of availability, performance, reliability, wear and tear, flexibility, and maintainability. The models in conjunction with the sensor data give the ability to predict the plant’s performance, evaluate different scenarios, understand tradeoffs, and enhance efficiency

Reference: GE Digital Twin: Analytics Engine for the Digital Industrial Transformation

Case Study 4: Predictive Maintenance in Transportation

The Greek Railways uses stored-inactive data, and uses the method
of data mining and applies machine learning techniques to create strategic decision support and draw up a risk and control plan for trains. They make an effort to apply Machine Learning open source software (Weka) to the obsolete procedures of maintenance of the rolling stock of the company (hand-written work orders from the supervisors to the technicians, dealing with the dysfunctions of a train unit by experience, the lack of planning and coding of the malfunctions and the maintenance schedule)

Reference: Predictive Maintenance Using Machine Learning and Data
Mining: A Pioneer Method Implemented to Greek Railways

Conclusion

These practical ML examples and case studies demonstrate the wide-ranging applications and benefits of machine learning in maintenance across different industries. They highlight how predictive maintenance can lead to significant cost savings, improved reliability, and enhanced operational efficiency.

Machine learning is revolutionizing the field of maintenance, offering unprecedented capabilities to predict, optimize, and streamline maintenance operations. From basic systems to sophisticated predictive models, the journey towards. Next we shall explore The Road Ahead for ML in Maintenance