Why Your Plant Needs Predictive Maintenance Software?

CEO, Power-MI
eventMay 12, 2023

In an increasingly competitive industrial landscape, industrial plants must focus on improving efficiency, reducing downtime, and optimizing asset management. A critical aspect of achieving these objectives is implementing predictive maintenance software. As a decision-maker in the maintenance department, it is vital to comprehend the importance of this technology and its potential to enhance your plant's operations. Moreover, managing analysts effectively is crucial in ensuring the successful implementation and management of predictive maintenance strategies. 

This article will delve into the role of predictive maintenance software in boosting the reliability of industrial plants, exploring the various types available, their integration with CMMS, and the benefits of adopting this cutting-edge technology.

Understanding Predictive Maintenance Software

Predictive maintenance software is a tool designed to help maintenance teams in industrial plants monitor, manage, calculate savings, and enhance the condition of their equipment and machinery. By leveraging cutting-edge data analysis techniques and efficient management practices, this software aims to minimize unexpected breakdowns, prolong equipment lifespan, and improve overall operational efficiency.

Predictive maintenance software streamlines the entire maintenance process, from scheduling and planning to execution and follow-up. It helps maintenance teams prioritize tasks, allocate resources efficiently, and track the progress of maintenance activities. By offering a centralized platform for managing all aspects of predictive maintenance, this software enables better communication and collaboration between maintenance personnel, analysts, and other stakeholders, leading to more informed decision-making and a more effective maintenance strategy.

Predictive maintenance software is a powerful tool that integrates data analysis and maintenance management to optimize the maintenance process. By proactively identifying potential equipment failures and streamlining maintenance activities, this software plays a critical role in enhancing the reliability and efficiency of industrial plants across various sectors.

Types of Predictive Maintenance Software

There are two basic types of predictive maintenance software: management and analysis. These two types serve distinct purposes, working together to create a comprehensive and effective organizational maintenance strategy. In this section, we will discuss the features and functions of both types of predictive maintenance software.

Predictive Maintenance Software for Management

Management-focused predictive maintenance software is designed to help organizations design, plan, and manage their predictive maintenance plans. This software streamlines various aspects of the maintenance process, improving efficiency and ensuring that maintenance activities are executed effectively.

Some key features of management-based predictive maintenance software include:

  • Reporting tools that enable maintenance departments to create reports for various predictive technologies in a unified format and failure catalog. This streamlines the reporting process and ensures consistency across different technologies.
  • Work order generation and tracking, which facilitate exporting to the CMMS and guarantee that work is completed on schedule. This feature helps keep maintenance tasks organized and ensures efficient resource allocation.
  • Asset condition history provides a comprehensive overview of equipment condition over time, including previous reports, work orders, and maintenance events. This information allows analysts to make informed diagnostics based on historical data.
  • Inspection route planning enables organizations to create and manage efficient routes for conducting equipment inspections, optimizing the use of maintenance resources and reducing downtime.
  • Analysts' calendars help organizations manage the schedules and availability of maintenance analysts, ensuring that their expertise is utilized effectively and contributing to better overall maintenance planning.
  • Failures records and statistics offer valuable insights into the most frequent failures per asset type, plant section, or time interval. This enables holistic analysis and identification of hidden failures causes, ultimately improving overall plant performance.
  • Root cause analysis tools support proactive maintenance by identifying the underlying causes of equipment failures and providing recommendations for addressing them. This feature promotes a more effective and efficient maintenance strategy by addressing the root causes of equipment issues.

Predictive Maintenance Software for Analysis

Analysis-based predictive maintenance software manages the data collected from portable devices or sensors installed on machinery and equipment. This data includes various parameters such as temperature, vibration, pressure, and other relevant indicators that help determine the health and performance of assets.

The software allows data analysis in one of two ways: either through specialized analysts or machine learning algorithms. In the case of specialized analysts, the software provides a platform for organizing and visualizing the data, enabling these experts to identify patterns, trends, and anomalies that may indicate potential failures or areas for improvement.

On the other hand, machine learning algorithms can automatically analyze the data, generating diagnostics and predictions about equipment condition and potential failures. These algorithms continually learn and adapt to the changing conditions of the equipment, ensuring that the software remains up-to-date and accurate in its assessments.

Some common features of analysis-based predictive maintenance software include:

  • Data visualization tools, such as graphs and charts that help analysts interpret the collected data more easily.
  • Customizable alert systems that notify maintenance teams of potential issues, allowing them to take corrective action before a failure occurs.
  • Integration with other data sources, such as production data, to provide a more comprehensive understanding of equipment condition.

Designing, Managing, and Optimizing Predictive Maintenance

Predictive maintenance is a dynamic process that requires continuous tuning and adjusting according to the condition of the asset fleet and the findings made during condition monitoring. It is a proactive, data-driven approach that allows maintenance teams to anticipate and address potential equipment failures before they become critical.

Designing a Predictive Maintenance Plan

The initial design of a predictive maintenance plan requires strategic organization of machines based on the appropriate taxonomy, criticality analysis, and failure mode analysis. Once sorted, predictive technologies and inspection frequencies can be assigned to each asset. The software should provide the ability to sort and analyze your assets effectively, forming the foundation of your predictive maintenance strategy.

Planning Routes and Assigning Analysts

Once the plan is formulated, the predictive maintenance team must create inspection routes. This strategic planning step is crucial, as it optimizes the use of resources and ensures comprehensive coverage of all assets. The routes should be assigned to analysts based on their expertise in specific predictive technologies and the availability of data collectors and assets. The software should facilitate this assignment, producing a clear and manageable calendar plan for each analyst.

Executing Predictive Maintenance and Managing Data

During the execution phase of predictive maintenance, data management and information analysis are key. Analysts should utilize the bundled analysis software, typically provided with the respective instrument, such as a vibration data collector, thermography camera, or ultrasound device, to analyze the collected data. Upon completing the diagnosis, the information should be structured and logged into the predictive maintenance software. This ensures that the asset condition, identified failures, and recommendations are captured as structured data, paving the way for optimized routes, inspection frequencies, and decision-making.

Managing Work Orders

Recommendations made in the reports are essentially maintenance work orders or notifications. These work orders should be exported to the Computerized Maintenance Management System (CMMS) for planning and execution. This is a crucial aspect of predictive maintenance that ensures all identified issues are addressed in a timely and efficient manner.

Verifying Work Order Execution and Calculating Savings

Once a work order is executed, predictive maintenance analysts should verify if all signs and symptoms of the potential failure have disappeared. If the potential failure disappears, the software should allow the recording of the case and calculation of savings. The ability to quantify predictive maintenance savings is a key feature of an effective predictive maintenance software.

Analyzing Failures and Identifying Patterns

Having recorded all potential failures identified during condition monitoring, the software should enable maintenance departments to conduct failure statistics. This allows them to identify the most common failures by asset type, plant section, predictive technology, or even by the analyst. Spotting failure patterns or repetitive failures leads to proactive maintenance actions.

Transitioning to Proactive Maintenance

Finally, a predictive maintenance software should provide tools for root cause analysis. This capability enables users to transition from predictive to proactive maintenance by identifying and addressing the root causes of equipment failures. This not only prevents repeated issues but also contributes to the overall optimization and efficiency of maintenance operations.

Integrating Predictive Maintenance Software with CMMS

Given the importance of managing work orders—from the creation of the predictive analysis report, through its execution in the CMMS, to the verification of work order execution and calculation of savings—it's clear that there's a critical need to integrate predictive maintenance software with the CMMS. This integration facilitates seamless communication between the two systems, streamlining the workflow and improving efficiency.

APIs: Bridging the Gap

The most common way to connect software platforms today is through Application Programming Interfaces (APIs), specifically RESTful APIs. An API is a set of rules and protocols that allows one software application to communicate and interact with another. It's like a middleman that delivers your request to the provider you're requesting it from and then delivers the response back to you.

RESTful APIs have become popular due to their simplicity, scalability, and ease of integration. These APIs use HTTP protocols and are based on a set of principles known as Representational State Transfer (REST). They allow different software systems to exchange data over the internet in a standardized way, making them ideal for integrating predictive maintenance software with a CMMS.

Analyzing and Transferring Data

The first step in integrating the two systems is identifying what data needs to be transferred between them. This could include information related to work orders and maintenance events. Both systems should be thoroughly analyzed to identify all necessary data elements for transfer.

Designing API Calls and Connectors

Once the necessary data elements are identified, the next step is to design the API calls and instructions. This process involves determining the requests that the API will make to retrieve or send data and the responses it will receive. A connector, which is a piece of code that enables communication between the API and the software application, also needs to be developed.

To successfully integrate the predictive maintenance software with the CMMS using an API, you'll need:

  • An Endpoint: This is the specific URL where your API can access the resources it needs from another software platform.
  • HTTP Methods: These are the actions that the API will perform. The most common methods are GET (retrieve data), POST (send data), PUT (update data), and DELETE (remove data).
  • Data Format: This is the structure in which data will be sent or received. The most common format is JSON (JavaScript Object Notation), which is easy to read and write.
  • Authentication: This is the process of verifying the identity of the user or system attempting to access the data. It's critical to ensure that only authorized users or systems can access your data.

So, Why Opt for Predictive Maintenance Software?

The shift towards a predictive maintenance strategy is becoming increasingly essential in industrial plants. The question arises, then: why should you consider predictive maintenance software? The answer depends on your current maintenance setup.

For those without a CMMS, implementing predictive maintenance software is a wise first step. It allows for the efficient management of machinery inspections, forming a solid foundation for maintenance operations. Once established, you can then look to integrate a CMMS, setting up a cohesive, streamlined maintenance management system.

For those already employing a CMMS, predictive maintenance software serves as a vital complement, filling in gaps left by standard CMMS features. It enhances your maintenance operations by providing functionality such as structured reporting with a failure catalog, statistical failure analysis, predictive maintenance savings calculation, inspection route planning, and asset condition history. Furthermore, predictive maintenance software offers the flexibility to grant access to external service companies, a feature often restricted by CMMS.

Finally, for those without an existing predictive maintenance strategy, implementing predictive maintenance software can be the catalyst for change. It allows you to design a predictive maintenance plan starting with simple visual inspections. The act of regularly visiting assets to perform an inspection checklist is an invaluable initial step toward implementing a comprehensive predictive maintenance program.

In conclusion, predictive maintenance software is a powerful tool, regardless of where you stand in your maintenance journey. Whether you are just beginning, have a CMMS in place, or are looking to elevate your existing predictive maintenance practices, predictive maintenance software can provide the structure, insights, and optimization needed to enhance the reliability and performance of your industrial plant.


What is Power-MI?

Power-MI is a cloud based solution that allows you to design & manage your condition-based maintenance plan integrating all techniques into one platform. Easy reporting, automatic work orders and CMMS integration.

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