Ready-Rate

Dear Colleagues!

I'd like to introduce you to the Ready-Rate system.

Ready-Rate is a software application designed to calculate the technical availability coefficient for machinery and equipment.

Table of Contents: Ready-Rate System and Predictive Maintenance

• 1. Introduction to the Ready-Rate System
  • What does the Technical Readiness Coefficient (TRC) indicate?
  • Why is this truly important?
  • What to do?
  • Transition from reactive to proactive management
• 2. Principles and Advantages of Ready-Rate
  • Systematic accounting, control, and risk management
  • Technical Readiness Coefficient: the foundation of reliable equipment management
  • Analysis of the TRC indicator
  • What the Ready-Rate system can cover
    • Machinery and mechanisms
    • Mining and heavy equipment
    • Production equipment and units
    • Downtime and repair logging
    • Ready-Rate Hierarchy
• 3. Technical Features of Ready-Rate
  • Working prototype and interface
  • Database and multi-user mode
  • Customization and adaptation capabilities
• 4. Advantages of Ready-Rate over Excel
  • Automation of calculations
  • Single source of truth
  • History and analytics
  • Access rights management
  • Scalability and reliability
  • Notifications and tasks
  • One-click reporting
• 5. Why start now?
  • Flexible solution
  • Savings and scalability
  • Decision justification
  • Collaboration discussion
  • Step-by-step implementation plan
  • Contacts
• 6. Predictive Maintenance
  • The essence of predictive maintenance
  • How it works with database data
    • Data collection
    • Data extraction and merging
    • Preprocessing and Feature Engineering
    • Machine learning model selection
    • Model training
    • Model evaluation
    • Deployment and request generation
  • Spare parts procurement request generation
  • Challenges and complexities

1. Introduction to the Ready-Rate System

What Does the Technical Availability Coefficient (TAC) Show?

It's a key indicator reflecting the actual technical condition of equipment, machines, and mechanisms. It's calculated monthly and shows the percentage of time that equipment was in good working order and operational.

Example: If a piece of equipment was operational for 600 out of 720 hours in a month, its availability coefficient would be 0.83 (or 83%).

Why Is This Really Important?

Every hour of downtime means direct and potentially significant losses.

When a mining dump truck, a maritime crane, a conveyor, or any other equipment stops, it's not just one machine that suffers; the entire production chain is disrupted: schedules are missed, people and resources are idle, and money is lost.

❗ Equipment downtime isn't just a pause; it's a real-time financial drain.

And what's especially important:

📉 Most breakdowns could have been predicted.

Accidents and delays often repeat. Wear and tear, operational errors, chronic failures – all of this can be tracked if data is collected, stored, and analyzed systematically.

What to Do?

To stop the chain of losses, you need to shift from reacting to managing:

• 📌 Don't wait for equipment to break down.
• 📌 Know its real condition.
• 📌 Predict failures in advance.
• 📌 Plan repairs and spare parts before a crisis hits.

This is the transition from reactive to proactive management. Don't just fight the "fire"; see where it's smoldering and extinguish it in advance. The Ready-Rate system is created precisely for this: so you know where equipment is failing – in advance, based on data, facts, and logic.

2. Principles and Advantages of Ready-Rate

Therefore, we propose moving from reacting to breakdowns to systematic accounting, control, and risk management:

• Plan equipment maintenance in advance.
• Plan and purchase spare parts and consumables at reasonable prices before equipment breaks down.
• Analyze technical availability coefficients for machines, systems, units, and components.
• Identify and evaluate weak links in the equipment fleet (inventory) in advance, based on real data.
• Track the condition of machines over time.
• Assess the reliability of machine groups.
• Compare the effectiveness of contractors, repair departments, and maintenance personnel.
• Obtain strong arguments for approving repair budgets and supplier purchases.
• Manage technical maintenance and repairs.
• Analyze trends and failure points to reduce long-term costs.
• Make informed technical and financial decisions.

The Technical Availability Coefficient: The Foundation of Reliable Equipment Management.

This indicator can be analyzed:

• By individual machines – to identify "problematic" ones.
• By groups of equipment – to understand the overall picture.
• By systems – for example, to determine that 60% of downtimes are related to hydraulics.

The indicator allows for high-precision determination of how much each unit or the entire fleet of machines is truly available for work. This means you can manage operational efficiency, reduce downtime risks, and optimize repair and maintenance costs.

All the logic of the Ready-Rate system is built upon this indicator.

What Can the Ready-Rate System Cover?

The system is designed to be scalable and universal – it can be adapted to any fleet of machinery and industrial equipment. Here are the main categories:

1. Machines and Mechanisms

Equipment involved in transportation, development, loading, and construction:

• Mining dump trucks
• Excavators
• Loaders
• Motor graders, bulldozers, rollers
• Other road construction equipment

2. Mining and Heavy Equipment

Equipment for heavy and continuous loads in mining and underground operations:

• Drilling rigs
• Percussion drills
• Tracked machines
• Underground machinery

3. Production Equipment and Units

Key elements of the production infrastructure on which the stability of the technological process depends:

• Belt and chain conveyors
• Pumping units
• Portal cranes, overhead cranes
• Transport lines and processing areas

The system easily expands to fit your specifics – from a single site to the entire production chain. All equipment is linked to data, events, technical systems, and reasons for downtime.

4. Downtime and Repair Accounting

• Unplanned and planned downtime and repairs.
• Classification of reasons: breakdown, waiting for parts, scheduled maintenance.
• Linkage to specific equipment, lines, or departments.
• Accounting by status: "In repair," "Waiting," "Ready for operation."
• Accounting for repair performers.

Ready-Rate Hierarchy

Ready-Rate uses a technical hierarchy:

• Machines and Mechanisms (inventory list)
• Equipment Systems (hydraulic, fuel, electrical, etc.)
• Units (pumps, reducers, drives)
• Components (filters, hoses, gaskets, sensors)

3. Technical Features of Ready-Rate

Currently, the system has a working prototype (see program screens below) with a web interface in Russian, Spanish, and English, accessible via a cloud link (I'll show it during a meeting).

PostgreSQL is used as the industrial database.

The system is multi-user, with the ability to differentiate access rights.

It's also possible to implement accounting for repair revenues and expenses in different currencies.

The system is already structured but requires further development, including the implementation of a coefficient calculation algorithm tailored to customer requirements, forecasting modules, and automation of requests.

This presents an excellent opportunity to adapt the program to your needs quickly and cost-effectively.

Ready-Rate is not a template; it's a flexible foundation for your actual operational environment.

Based on your technical specifications, the program will be adapted precisely to your infrastructure.

4. Advantages of Ready-Rate over Excel

Using Excel for accounting is only a temporary solution. It's suitable at the start but quickly becomes a bottleneck as data volume, equipment quantity, and analysis complexity grow. A specialized system like Ready-Rate provides a fundamentally different level of management. Here are the key advantages:

• 1. Automated Calculations: Coefficients are calculated automatically—no manual formula entry, no risk of error. The program considers all parameters: downtime, repair statuses, equipment types, and malfunction history.
• 2. Single Source of Truth: Data is centralized; it's not scattered via email, lost in various files, or inconsistent across versions. All users work within one system with current information.
• 3. History and Analytics: The system stores and analyzes long-term statistics, allowing you to identify trends, problematic units, and recurring failures. Excel loses manageability and speed with such volumes.
• 4. Access Control: You can assign roles: a mechanic sees one thing, a manager another, and an accountant a third. Excel doesn't provide secure multi-user interaction.
• 5. Scalability and Reliability: The program scales from a single site to the entire enterprise. It has a reliable database (PostgreSQL), backup capabilities, and the potential for integration with other systems.
• 6. Notifications and Tasks: Built-in reminders for planned maintenance, spare part request statuses, and deviations. Excel won't remind you of anything if it's forgotten.
• 7. One-Click Reporting: Everything is ready for generating reports, graphs, and summaries—no manual assembly or filtering needed. This saves a significant amount of time.

🟩 Ready-Rate isn't just a spreadsheet; it's a digital assistant that works 24/7, doesn't make calculation errors, and helps you make informed management decisions.

5. Why Start Now?

• 🧩 You're not getting a "box"; you're getting a solution that lives by your rules.
• 💰 Savings begin immediately—thanks to a precise understanding of equipment status, failures, and expenses.
• 📊 The system scales—from an individual site to the entire company.
• 📈 The analytics module helps justify repair budgets, spare parts and material requests, and management decisions based on real data, not assumptions.

No assumptions or guesswork—just figures, argued and substantiated.

Ready to Discuss Collaboration

Together, we'll define development priorities, the accounting approach, analytics, user roles, and the necessary functionality.

I'm ready to offer a step-by-step implementation and development plan, including:

• Launching a pilot site.
• Gathering feedback.
• Setting up directories and structure.
• Connecting analytics and calculations.
• User training.

Let's develop a system together that will work for you—predicting, informing, preventing, and optimizing.

All capabilities are in one interface, accessible from any browser. I'm ready to show you the prototype, discuss your tasks, and propose the first steps.

Contact me, and we'll find an approach that works best for you.

Contacts

Web page: https://teuworld.com

LinkedIn: https://www.linkedin.com/in/olegzhikharev/

Telegram: @ozhikharev

6. Predictive Maintenance

Predicting machine and mechanism failures (Predictive Maintenance)

Essentially: Instead of waiting for equipment to break down (reactive maintenance) or replacing parts on a schedule (planned preventive maintenance), predictive maintenance uses data to forecast when a failure is likely to occur, allowing maintenance to be performed exactly when needed.

How it works with database data:

• Data Collection:
  • Historical failure data: Records of previous failures are needed from the database: date, type of failure, affected components, possibly the conditions under which the failure occurred.
  • Equipment condition data: This can be the most challenging part if the database doesn't contain such data. Ideally, you'd have sensor data (temperature, vibration, pressure, energy consumption, operating hours, number of work cycles, etc.). If such data isn't available, you can use indirect indicators: equipment age, hours since last service, type of part used, supplier data, etc.
  • Maintenance data: When the last service was performed, what was replaced, who performed it, how long it took.
  • Spare parts data: What part was used, its cost, service life.

  We assume that all these data (or at least part of them) are stored in various database tables.

• Data Extraction and Integration:
  • SQL queries are used to extract relevant data from different tables (e.g., JOINs) to create a unified dataset where each row represents the state of a machine at a specific point in time or a record of a breakdown/service.
  • Loading this data into a Pandas DataFrame.
• Preprocessing and Feature Engineering:
  • Time-series analysis: If you have sensor data, these will be time series. You'll need to extract features from these series: averages, moving averages, standard deviations, trends, peaks, frequencies (using Fourier transform for vibrations) within specific time windows.
  • Creating "Remaining Useful Life" (RUL): For each machine state record, if a failure followed, you can calculate how many days/hours/cycles remained until failure. This will be the target variable for a regression task.
  • Creating a binary target variable: If you need to predict "will it break down in the next X days," the target variable will be 0 (no) or 1 (yes). This is a classification task.
  • Handling categorical data: Machine types, failure types, part types, suppliers, etc.
  • Handling missing values.
  • Scaling numerical features.
• Machine Learning Model Selection (using TensorFlow/Keras or other libraries):
  • For RUL prediction (regression):
    • Linear Regression (if the dependency is simple).
    • Random Forest Regressor.
    • Gradient Boosting (XGBoost, LightGBM).
    • Neural Networks (Dense layers in Keras) - well-suited if there's a lot of data and dependencies are nonlinear.
    • LSTM/RNN (if data highly depends on time sequence, e.g., sensor readings).
  • For predicting future breakdowns (classification):
    • Logistic Regression.
    • SVM (Support Vector Machines).
    • Random Forest Classifier.
    • Gradient Boosting.
    • Neural Networks (Dense layers, with Softmax or Sigmoid activation on the last layer).
• Model Training:
  • Splitting data into training, validation, and test sets.
  • Training the selected model on the training data.
• Model Evaluation:
  • Evaluating the model's performance on test data using appropriate metrics (e.g., MSE, MAE for regression; accuracy, precision, recall, F1-score, ROC-AUC for classification).
• Deployment and Request Generation:
  • Integration: Developing a mechanism that will periodically retrieve current machine status data (again, possibly from the database or directly from data sources).
  • Prediction: The loaded model will use this current data to generate predictions (e.g., "Machine X has an 85% probability of breaking down in the next 7 days") or "Machine Y has a Remaining Useful Life of 30 days".
  • Request Generation: If the prediction indicates a high probability of failure or an approaching end of service life, the system automatically generates a request.
    • Request type: Purchase order for a specific spare part, request for planned maintenance, request for diagnosis.
    • Prioritization: Depending on machine criticality, breakdown cost, etc.
    • Automation: The request can be automatically sent to the inventory management system or the Computerized Maintenance Management System (CMMS).

Generating Spare Parts Purchase Requests

This is a direct consequence of forecasting:

• Identification of necessary spare parts: When the model predicts the failure of a specific component, you know which spare part will be needed for repair.
• Inventory check: The system checks current stock levels in the warehouse (inventory data can also be in the database).
• Quantity determination: If stock is insufficient, the necessary quantity for the order is calculated (considering delivery time, buffer stock, etc.).
• Order generation: A purchase request is formed including:
  • Part name
  • Part code
  • Quantity
  • Estimated need date
  • Reason for order (predicted failure of machine X)
  • Estimated supplier (if historical data exists)

Challenges and Complexities:

• Data Quality: "Garbage in, garbage out." If data in the database is incomplete, inaccurate, or contradictory, the model will perform poorly.
• Sensor Data Availability: If sensor data is unavailable, you'll have to rely on less informative features (operating hours, age), which will reduce prediction accuracy.
• Class Imbalance: In failure data, there are usually very few "positive" examples (actual failures) compared to "negative" ones (normal operation). This requires special training techniques (e.g., oversampling, undersampling).
• Integration: Connecting a Python system to existing accounting and management systems (if any) can be a complex task.
• Continuous Learning: The model must be periodically retrained on new data to account for changes in equipment operation, the appearance of new failures, etc.

Despite the complexities, a predictive maintenance system based on data from your database (in combination with other sources) can significantly reduce downtime, optimize spare parts inventory, and lower maintenance costs.