Predictive Maintenance Solutions and Analytics

Ans. Our IoT team has experience in partnering with global customers, to develop reliable and efficient Predictive Maintenance systems. We collaborate with customers based on the following business models:

• Complete Solution Package: Under this model, we will be involved in the Design, Development, Maintenance and Upgrade of IoT Predictive Maintenance solution for your industrial assets.
• Develop and Transfer Package: In this model, we design and develop the Predictive Maintenance solution and deliver it to your in-house team. Post-deployment, your in-house IT team can take charge of the maintenance and operation of the entire system. We can partner with your teams for any specific upgrade (a new framework to be included, a new tool to be integrated, etc.). Additionally, under either of the mentioned engagement models, customers can also subscribe to our solution upgrades, that are released periodically, by paying the subscription charges.

Ans. In our Predictive Maintenance Solutions, we support multiple channels to alert the maintenance team about a possible machine failure or a maintenance issue. We can inform your maintenance and support teams through:

• Email alerts, or
• Text based alert messages via any of the standard messaging applications such as SMS or WhatsApp

Ans.A Predictive Maintenance system is based on a reliable, information-intensive model for industrial asset Management. You can use the real-time information about your industrial assets to enhance your business offerings and gain competitive advantage. The Predictive Maintenance analytics information can be leveraged for:

• Identifying ‘When’ & ‘How often’ you want to service the equipment. Thus identifying a maintenance schedule, that enhances asset availability & productivity.
• Learning about the failure conditions of your industrial assets, in detail. This includes having a better knowledge of the possible failure types; root cause analysis of the failure; any additional metrics to clearly evaluate the conditions of particular industrial equipment.
• To improve & optimize the design of your industrial equipment to overcome any faulty behavior based on the predictive analytics data. This will greatly trim down your bottom line expenses.
• The historical data can be used to predict the performance behavior of the equipment and the production line under different conditions. This will help you preempt any major fault by identifying and reporting even a minute anomaly in equipment behavior, to avoid downtime

Ans. Our team behind IoT Predictive maintenance Solution comprises of:

• Cloud Computing Experts
• Hardware Engineers
• Network design Engineers
• IoT Architects and developers
• Big Data Experts
• Embedded Firmware developers
• IoT based connectivity protocol Experts

Ans. One of the main challenges associated with developing an IoT based Predictive Maintenance Solution is the fact that there is no universal/ one-size-fits-all predictive maintenance framework. Every industry facility is unique, with its own set of “specialized information” to be collected for reliable Enterprise Asset Management. To achieve desired business objectives, a Predictive Maintenance solution needs to be tailor-made for the specific industrial use case, taking into account the behavior and design parameters of the production line. Thus, the success of the Predictive Maintenance project is dependent on the following factors:

• Identifying the data collection and data management requirements – this includes defining what data is to be gathered and planning how and where (on-cloud or on-premise) the data will be processed.
• Defining the metrics and parameters to monitor the industrial equipment, as this forms the basis of the entire maintenance operations.

Ans. The decision to choose between an On-premise & On-cloud model for storage & processing of your industrial asset data depends on the following factors:

• The allocated budget for the project
• Annual operating costs
• How much and what all type of data is to be stored (like real –time data, historical data etc.)
• Number of times devices or equipment are used daily for operations (this is important to identify its criticality).
• Number of times the equipment has to be analyzed.

Ans. Our Predictive Maintenance solutions can coexist with the legacy industrial assets and production systems as long as there is a well-defined software protocol that gathers the data, externally from the equipment.

Ans. At Embitel, we have developed in-depth expertise in filtering techniques (using Python script and statistical models) necessary to identify relevant data from the humungous amount of raw data. Our teams have deep understanding of the data cleansing techniques and they ensure that the filtered data is error-free and reliable

Ans. The criticality of accuracy of the results delivered by Predictive Analytics varies as per the use-case.


For example, 99% accuracy in predicting equipment failure is very critical for safety-intensive applications such as medical equipment, automotive applications, factory shop-floors, etc.


We can help you achieve 99% accuracy in predicting equipment failure, provided we have access to a large volume of valid data sets (historic data- error logs, failure and successful events).


If the volume of available and relevant data logs is less, then 85-95% accuracy can be achieved.

tanding of the data cleansing techniques and they ensure that the filtered data is error-free and reliable

Ans..Some of the common issues associated with predictive maintenance are listed below:

• Poor data collection – This could include limited data or low-quality data related to asset failures.
• Prediction errors – If the accuracy of the predictive maintenance solution is not tuned properly, there could be prediction errors.
• Security concerns – Organisations face multiple challenges in ensuring data management and security. The predictive maintenance solution provider and administrators should ensure that external parties are not authorised to access the system software.