How to Leverage Big Data for Predictive Maintenance?

Article Written by:

Ganesh Veerappan

Leveraging Big Data for Predictive Maintenance

Harnessing the Power of Big Data for Predictive Maintenance

In today's technology-driven era, one phrase often heard in corporate corridors and boardrooms is 'Big Data.' Big Data refers to the vast volumes of data generated every second from myriad sources, captured in diverse formats. Its potential to revolutionize businesses is unparalleled, more so in maintenance operations, where it has a critical role, especially concerning predictive maintenance (PdM).

Predictive Maintenance: An Overview

Before we delve into Big Data's role, let's understand what predictive maintenance entails. PdM signifies an advanced level of maintenance strategy. Unlike preventive maintenance that operates on predetermined schedules or reactive maintenance that awaits machinery failure, PdM banks on data-driven insights. It foresees when equipment might need maintenance and steps in at the right moment to prevent failure, thereby minimizing downtime and optimizing asset lifespan and productivity.

Big Data's Crucial Role in Predictive Maintenance

Big Data is the engine that powers predictive maintenance. It encompasses extensive information from various sources - machine logs, sensor data, maintenance records, temperature readings, vibration patterns, and more. This vast array of data is used to extract valuable insights that can significantly enhance predictive maintenance programs' efficacy.

Machine Learning (ML), a subset of artificial intelligence (AI), is frequently used for this Big Data analysis. ML algorithms can recognize intricate patterns and trends that might evade human analysis. These algorithms can predict potential faults and failures by learning from past and real-time data.

Benefits of Big Data in Predictive Maintenance

The primary advantage of Big Data in PdM is averting failure, but it doesn't stop there. It can also optimize maintenance schedules and resource allocation. By accurately predicting an asset's time-to-failure, maintenance can be scheduled precisely when necessary, avoiding unnecessary interventions and maximizing asset uptime, leading to cost savings in labor, spare parts, and improved productivity.

Challenges and Solutions in Leveraging Big Data

Using Big Data for predictive maintenance presents challenges, such as data collection, cleansing, and organization from varied sources and formats. Data security and privacy measures are critical to protecting sensitive information, and skilled personnel must manage and interpret the data.

Advanced CMMS (Computerized Maintenance Management System) solutions can address these challenges. CMMS platforms integrate with IoT devices, collect real-time data, and use built-in AI and ML capabilities to process and analyze this data. They provide predictive insights in an understandable format, eliminating the need for specialized data analysis skills.

For instance, consider a manufacturing plant in India grappling with frequent breakdowns in one of its machines, causing costly unplanned downtime. They decide to implement a CMMS platform with predictive maintenance capabilities. Sensors installed on the problematic machine continuously collect data (temperature, pressure, vibration, etc.) and feed it to the CMMS. The system's AI algorithms analyze this data, identify patterns leading to breakdowns, and predict the next failure. The maintenance team is alert and can intervene before the machine breaks down again, saving considerable downtime and cost.

Numerous big data solutions are available in the market designed specifically to handle and analyze IoT data. Here are a few examples:

IBM Watson IoT
IBM's Watson IoT platform is designed to extract valuable insights from your IoT data. It provides real-time analytics and machine-learning capabilities that can be used for predictive maintenance. It offers tools for data integration, secure device connectivity, and application development, making it suitable for various industries.
Microsoft Azure Stream Analytics
This service from Microsoft offers real-time analytics and complex event processing. It analyzes and visualizes streaming data from IoT devices, applications, and more. It integrates with other Azure services, allowing you to use it as part of a broader data analytics system.
Google Cloud IoT
Google's Cloud IoT is a comprehensive set of tools that connect, process, store, and analyze data at the edge and in the cloud. The platform consists of several modular cloud services that work together, offering services like Cloud IoT Core (for device connectivity), Cloud Pub/Sub (for event-driven computing), and Cloud Functions (for serverless environments).
SAP HANA is an in-memory data platform suitable for performing real-time analytics and developing and deploying real-time applications. It's particularly useful for businesses that want to process large amounts of IoT data in real time.
Oracle IoT
Oracle offers robust IoT applications that allow businesses to connect, analyze and integrate their operational technology (OT) data with business processes. Its IoT cloud solutions offer real-time data analysis, endpoint management, and high-speed messaging that help businesses to make data-driven decisions.
AWS IoT Analytics
AWS offers a fully-managed service that makes it easy to run sophisticated analytics on massive volumes of IoT data. You can collect, process, enrich, store, and analyze your IoT data at scale.
Dell Boomi
Dell Boomi is an integration platform (iPaaS) service that provides cloud-based data and application integration. It offers pre-built connectors for various data sources, making connecting IoT devices to analytics software easy.
Predix, by GE, is a purpose-built platform for industrial IoT. It offers a comprehensive approach to data management, analytics, and machine learning, designed specifically for industrial data.

Choosing a solution that best fits your organizational needs is essential, considering factors such as the volume and type of data, security requirements, the desired level of automation, and your existing IT infrastructure. Each platform has unique features and benefits, so take the time to research and evaluate each based on your specific requirements.

Several predictive analysis models can be utilized in the industrial sector, each suited for different kinds of data and predictions. Here are some of the most widely used ones:

Linear Regression Models
These models are used when the relationship between the input and output variables is linear. They can predict a continuous output variable based on one or more input variables.
Logistic Regression Models
These are used for binary classification problems, where the output variable can take two values (for example, a machine failure: yes/no).
Decision Trees
Decision tree models are powerful tools that handle categorical and numerical data and are often used for classification and regression tasks.
Random Forests
Random Forests are an ensemble learning method that operates by constructing multiple decision trees at training time and outputting the class that is the mode of the classes for classification or mean prediction for regression.
Support Vector Machines (SVM)
SVMs are models used for regression and classification tasks. They are particularly useful when the data is not linearly separable.
Neural Networks and Deep Learning Models
Neural networks can model complex patterns effectively by 'learning' from the data, making them suitable for tasks such as image and speech recognition, natural language processing, and predicting complex industrial processes.
Time Series Models (ARIMA, SARIMA, LSTM, etc.)
These models are used when the data has a temporal component. They are widely used in forecasting tasks, such as predicting energy consumption, stock prices, or machinery failure based on historical data.
Survival Analysis Models
In industrial settings, these models can be used to predict the 'time to event,' which is often the time to failure for a piece of machinery or equipment.

Remember, the choice of model depends heavily on the nature of your data and the specific problem you are trying to solve. It is not uncommon to try out several models and choose the one that offers the best performance in accuracy, speed, and interpretability, per the problem's requirements.

The effective use of big data for predictive analysis requires several steps: defining your goals, data collection, data preprocessing, model development, model validation, and finally, deployment and monitoring. Let's illustrate this with an example scenario:

Let's consider a manufacturing company called 'IndoTech' that operates multiple production lines. The company has recently started to experience frequent machine breakdowns that disrupt production, cause delays, and increase costs.

Step 1: Define Goals

IndoTech aims to predict machine failures before they occur to minimize downtime, maintain production efficiency, and reduce costs. They aim to use big data predictive analysis to achieve this goal.

Step 2: Data Collection

IndoTech collects data from various sources, including machine sensors (IoT data), production logs, maintenance records, and operator observations. This data is stored in a big data platform that can handle volume, velocity, and variety.

Step 3: Data Preprocessing

The collected data undergoes cleaning, normalization, and transformation to prepare it for analysis. This step is crucial to ensure that the data fed into the predictive model is accurate and relevant.

Step 4: Model Development

IndoTech's data science team applies machine learning algorithms to develop predictive models. These models use historical data to identify patterns or signs that indicate an impending machine failure.

Step 5: Model Validation

The predictive models are tested and validated using some of the collected data not used during model development. This step is to ensure the model's accuracy and reliability.

Step 6: Deployment and Monitoring

Once validated, the predictive models are deployed into the production environment. The models analyze the incoming data in real time and predict potential machine failures. If a risk is identified, alerts are sent to the maintenance team for preventive action.

Step 7: Continuous Improvement

The performance of the predictive models is monitored over time. As more data is collected, the models are continuously updated and improved for better accuracy and reliability.

By following these steps, IndoTech successfully implemented big data predictive maintenance, improving machine reliability, reduced downtime, and cost savings. This scenario demonstrates the power of big data and predictive analysis in proactive maintenance and improved operational efficiency.

Conclusion: Big Data – The Game Changer for Maintenance Management

When efficiently utilized for predictive maintenance, Big Data can save high costs, prolong asset lifespan, enhance safety, and boost overall productivity. The crux is choosing the right CMMS solution to exploit Big Data's potential while overcoming its challenges. After all, in the maintenance world, if you can predict the future, you can rule the present!


Big Data Analytics for Predictive Maintenance Strategies

Predictive Maintenance Explained

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Amrapali Exports - Esteemed Clients of cryotos
Anuraj Sharma - Maintenance Manager

The best thing about Cryotos CMMS software is that it always tells me when something needs to be fixed - before it breaks and cost me a fortune!

The software is very user-friendly, and even if I don't know how to do something, the support team is always there to help me out.

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Jeevan Belliappa - Head, Information Technology

Since we've been using Cryotos CMMS Software, our asset maintenance management has been running more smoothly than ever. We're able to track and optimize our preventive maintenance with ease, thanks to the customized process workflow. This has been a lifesaver for our team!

The Cryotos CMMS software has allowed us to manage our assets more effectively and efficiently. It has helped us to improve our asset utilization. The software is user-friendly and easy to navigate, and the support team is always quick to help with any questions we have. We're very happy with the results we've seen so far and we're confident that we'll continue to see improvements in our asset management as we use the Cryotos CMMS software.

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Cryotos goes above and beyond when it comes to customer satisfaction. Planys Technologies use Cryotos for preventive/ corrective maintenance of our critical systems and not once did we face a problem that was not dealt with immediately. The team is extremely professional and helpful. We can rely on Cryotos when it comes to the operations and processes.

Thanks to the whole team for the amazing performance and thought leadership. It's rare to see teams that can put themselves in the customer's shoes and think from their perspectives - Cryotos team doesthat and more.

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On behalf of Xpert FMS we would like to express our sincere appreciation for the excellent support Cryotos team have provided us for our building and facility management. Cryotos has always provided support that is timely and meets our expectations. Professionalism and quick response are the hallmarks of this team.

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ST Jude India Childcare Centers - Cryotos Esteemed Client
Mukesh J - CIO

We are able to raised the workorder and tracking of these till the closure as helped us to provide better services our beneficiaries. Workflow-based work order management has helped us to define processes for each locations separatley. System provide notification once order is raised for assignment to engineer.

Maspro - Esteemed Clients of Cryotos

This CMMS software is top-notch! I can't believe how much it has helped me with my facilities maintenance management. Customized reports and business intelligence have been invaluable in making better decisions.

The software offers a great deal of customization for each organization's specific needs. They are always updating the software with new features and improvements, which is great. The support team is fantastic - they are always quick to help with any questions or problems we have. This software has definitely made my life a lot easier! It's so easy to use and it's really helped me keep track of all my maintenance tasks in one place. I would definitely recommend it to anyone looking for a CMMS platform.

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Mr. Vishnu S Nair

Since we've started using Cryotos, our team has been able to stay on top of all the various SOPs. Checklists and safety procedures are now easily accessible to everyone, and we can quickly reference them whenever we need to. The software has definitely helped to improve our team's communication and efficiency. Plus, the fact that it's cloud-based means we can access it from anywhere, which is a big plus. It has been a great help in keeping everyone on the same page and up to date with the latest procedures.

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B S Vijaya K. - Maintenance Engineer

At starting it felt some what difficult for scheduling but now it made to easy access.

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Nithin G. - Maintenance Incharge

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Easy to access and data collection,user friendly, the technical team and the supporting team is excellent the way they are solving the issues ontime and the continuous training given by supporting team. I would like to promote the cryotos APK to other industries to use.

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Easily we came to know which machine is problem and what is the issue. And we got the exact data of spare parts stock. Using cryotos APK our machinery maintenance is increased and now we are getting less machines problem.

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Aptiv8 IT Solutions
Hashim - Business Project Manager

This CMMS software is top-notch! I can't believe how much it has helped me with my facilities maintenance management. Customized reports and business intelligence have been invaluable in making better decisions.

The software offers a great deal of customization for each organization's specific needs. They are always updating the software with new features and improvements, which is great. The support team is fantastic - they are always quick to help with any questions or problems we have. This software has definitely made my life a lot easier! It's so easy to use and it's really helped me keep track of all my maintenance tasks in one place. I would definitely recommend it to anyone looking for a CMMS platform.

Gurit - Esteemed Clients of Cryotos
Mr. Mohanmurali R

I recently purchased Cryotos CMMS Software for asset management needs at my organization and after a few months of testing and monitoring, I'm incredibly pleased with the results. The software is user friendly, accessible and the design is very intuitive. It covers all the aspects of managing maintenance and repair, starting from preventive maintenance and standardizing operation cycles, to scheduling and streamlining workflows. I am highly satisfied with the performance and the range of features offered by Cryotos. Highly recommendable!

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Mr. Sanjay Beri

Before using Cryotos, asset management and maintenance seemed like an uphill task for my team. Manual processes laden with human errors did not work for us at all. We decided to go with Cryotos after checking out similar products in the market.

The user interface of the software is intuitive and easy to use, making it simple to navigate through the different features and functionalities. I particularly appreciate the ability to track all assets in real-time, allowing me to keep a close eye on their performance and maintenance needs.

Customized work order and workflow creation has reduced the needs to always follow a particular template. Depending on our needs we can create tailor made workflows to suit our requirements.

I highly recommend Cryotos to anyone who would like to unburden themselves of maintaining and managing their critical assets.

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Dinex - Esteemed Clients of Cryotos
Mr. Yuksel Aksin

As a customer of Cryotos CMMS software for asset management needs, I must say that I am thoroughly impressed with the features and functionality offered by the platform. Cryotos CMMS software has made managing my assets a breeze, and I no longer have to worry about missing maintenance or inspections.

One of the standout features of Cryotos CMMS software is the ability to generate work orders and schedule maintenance tasks. This has made it easy for me to stay on top of maintenance and repair tasks, ensuring that my assets are always in optimal condition.

The software also allows me to track inventory levels which has streamlined my inventory management process. The ability to generate reports and analyze asset performance data has also been invaluable in helping me make data-driven decisions about my assets. Overall, I would highly recommend Cryotos CMMS software to anyone looking for a reliable and efficient asset management solution.

Mr. Sathyendran R.

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I am using this application right now for my service team, very good, user friendly, have lot of customisation in workflow and service reports. the team supported for initial implementation, They have good support team. Myself fully recommended this application.

What do you dislike about Cryotos?
Noting as of now seems all good

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Cryotos application is solving our feild service team operations.

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Mr. kandasamy S.

What do you like best about Cryotos?
Excellent application for Maintenance activity like PPM,etc
Good team for develop our process as per our requirement like process,Standards,etc
Professional & very Supportive team.
Really appreciated for language change (English to Arabic).

What do you dislike about Cryotos?
There is no negative observation till now.

What problems is Cryotos solving and how is that benefiting you?
Scheduling & PPM for Maintenance Activity.

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Mr. Yap

What do you like best about Cryotos?
template are ready to use, minimal need for IT knowledge. support team are supportive in all aspects

What problems is Cryotos solving and how is that benefiting you?
We are still in the midst of experimenting all the functions in Cryotos, for now is the ease of using asset management and as a scheduler.

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Mr. Hari K. - Ram Leathers

What do you like best about Cryotos?
Asset Management - Is Very easy for us, which make us to easily complete the tasks.

We are able to schedule the tasks and at the same time able to capture the daily readings for the machine.

Meter Reading module provides the real time data monitoring for all assets.

The very specific feature of the software is "AMC Monitoring"

What do you dislike about Cryotos?
As of now this software provides a better solution for us.

What problems is Cryotos solving and how is that benefiting you?
Asset monitoring is very helpful.

Able to track the machine availability using the MTTR & MTBF details.

MIS Dashboard - Provides the breakdown information of all our machines.

The dahboards & reports generates the accurate details our assets.

They also provide the customizable dashboard option using Metabase.

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Dinex - Esteemed Clients of Cryotos
Māris A.

What do you like best about Cryotos?
We used many maintenance CMMS and only in Cryotos is functionality to build your own workflows for work orders and scheduled maintenance. With this CMMS we not need some separate analyzing system like Power Bi, Cryotos have already built-in Metabase system where we can easily build reports and visualizations for reports.

What problems is Cryotos solving and how is that benefiting you?
CMMS help us control all machinery brekdowns and factory problem. CMMS collet all data about our historical data, its help analize deviations. With this system we build all necessary processes which we need in our factory, which are work autonomous.

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Planys - Cryotos Esteemed Client
Aravindishwar k.

What do you like best about Cryotos?
Cryptos is very easy to use and user friendly which we can easily customise to our needs and while delivering the workprocess it's very convenient to use by the end user as well.

What problems is Cryotos solving and how is that benefiting you?
Cryotos saves our time and space thus act as a great inventory of data which will be accesed from anywhere any time and it helps us to track the work we have done all along it's a best CMMS software.

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