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Predictive Maintenance Solutions and Analytics

Predictive Maintenance solutions are manifestations of artificial intelligence, IoT sensors and data science, working in tandem, to optimise the monitoring and maintenance of assets. The assets can be field-deployed IoT devices like solar panels or the parts of an automobile. Most of these assets do not experience sudden breakdown that impedes their functionality. The breakdown of an asset takes time and usually there are multiple warning signals along the way. However, the early warning signs may not come into the notice of the human operator/administrator/driver. This is where predictive maintenance solutions can be beneficial. Such solutions include monitoring technologies that enable:
  • Collection of real-time asset data
  • Processing of data using machine learning algorithms
  • Release of warnings/alerts to the administrator in case of any abnormalities
This allows the administrator/driver to schedule the maintenance of the asset/vehicle part and make repairs even before the failure happens. Predictive maintenance in Industrial IoT helps in reducing unplanned asset downtime and boosts the efficiency of the plant.

Our Predictive and Preventive Maintenance Solution Expertise

In Embitel’s IoT implementation journey of more than 16 years, our IoT team has custom-designed and developed industrial, enterprise and automotive predictive maintenance solutions for customers across India, USA and Europe.

  • Industry 4.0 – Solar Energy tracking system, Industrial drive controls
  • Enterprise – Enterprise Battery Management Systems (BMS)
  • Automotive – Predictive Maintenance (PdM) for vehicle parts

The business benefits that can be derived from our IoT predictive maintenance solutions include:

  • Higher asset availability
  • Improved work-force productivity
  • Optimized energy consumption
  • Lower operational costs
  • A fool-proof asset management solution at your disposal

Development Services for Cloud Based Predictive Maintenance Solutions

Our Predictive Monitoring, Analytics and SCADA solutions are custom-designed for Asset Management, based on your unique business requirements. All our PdM solutions are designed based on the Internet of Things (IoT) Technology Stack. Listed below are our services pertaining to IoT Predictive Maintenance to help our partners achieve a winning RoI:

Expertise in IoT Tools and Technologies

  • Message Queuing Telemetry Transport (MQTT): Regarded as a very versatile and lightweight protocol, MQTT is ideal for environments that allow optimal bandwidth usage. MQTT protocol has minimal code footprint and can run on any type of operating systems.
  • NarrowBand IoT (NB-IoT): Designed for applications that require the transmission of small chunks of data over longer periods of time, NB-IoT technology consumes less power, is easy to deploy, offers extended long range coverage and is very reliable and secure.
  • Open Platform Communications (OPC): Open Platform Communications or OPC is one of the most widely used protocols for the reliable and safe exchange of data. OPC is a great value addition to an IoT system as it can facilitate safe streaming of data to desired destinations such as a cloud app or a third-party app. Some of the data types captured in OPC are:
    • Real time parameter data
    • Historical Data
    • Alarm and alerts
    • Commands

IoT Predictive Maintenance Customer Success Story

Find out how we are partnering with industry leaders to create intelligent, fool-proof industrial maintenance systems using Predictive Maintenance (PdM):

What are the Main Components of Predictive Maintenance Process?

Main Components of Predictive Maintenance Process
Here are the primary components of our predictive maintenance solutions:
  • Sensor Network for Data Collection: A powerful network of IoT sensor nodes is integrated with the assets to constantly monitor their condition. These IoT sensors collect real time data regarding current health of the assets. The collected data is compared with the pre-configured threshold values to detect or predict malfunctions.
  • IoT Gateway Hardware and Software: Microcontroller Hardware board and software design of the IoT Gateway can be custom-made as per the project requirements. The IoT gateway acts as a communication bridge between the IoT sensor nodes and cloud back-end.
  • Machine Learning for Predictive Analysis: Raw data from sensors is converted into actionable insights at the IoT Cloud.
    • The raw data is filtered to identify relevant information.
    • Depending on the project requirements, predictive maintenance algorithms (Machine Learning, Deep Learning, etc.) can be integrated with the Cloud Application. The data is processed and analysed using Machine Learning models and AI tools (based on project requirements), to accurately predict equipment failure.
    • The Cloud backend also hosts databases and an interface is designed to manage integrated third party systems.
  • Mobile and/or Web Interface: With an operator-centric HMI/UI, the mobile app and web dashboard act as the central control unit for managing the assets. Data is made available in real-time and user-role management, report generation and other plugin integrations can be customized as per the requirements.

[IoT Video] How do Predictive Maintenance Solutions Work?

What is the business engagement model for Predictive Maintenance solutions development?
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.
How is the maintenance team alerted about a possible fault or hazard?
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
Based on your Industrial Maintenance use-case, we can implement all the necessary alert mechanisms.
In what ways can our organization leverage the Predictive Maintenance Information to gain competitive advantage?
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
Can you share details regarding the skill-sets and expertise of the software and hardware development team behind your Predictive maintenance solutions?

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
What are the challenges in Predictive Maintenance? How have you been addressing them?
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.
At Embitel, our IoT experts conduct detailed workshops for our clients to discuss and define their industrial asset Management goals and design a customized IoT Predictive Maintenance solution for them.
On-Premises or on-cloud – Which one is ideal for my business? Where does the data analysis take place- on the physical servers or on the cloud?

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.
Is it possible to integrate your Predictive maintenance solutions with the legacy industrial assets and production systems?
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.
How do you identify the relevant data from the huge volume of Raw Data generated by the systems?
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
What is the accuracy of results delivered by your Predictive Maintenance solutions?

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

What are the common problems with Predictive Maintenance?
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.
The above problems can be easily mitigated if you partner with a reliable PdM solution provider.

IoT Predictive Maintenance – Knowledge Bytes

Knowledge bytes

NarrowBand IoT or NB-IoT is one of the prominent mobile Internet of Things Technologies that offers a cost-effective & low power wide area connectivity.

Of late, Narrow Band IoT has found extensive application in industrial automation solutions, especially for predictive monitoring and Industrial Asset Management.

The following are some of the salient features offered by Narrow Band IoT technology.

  • Higher penetration power: NB-IoT can be used to connect IoT sensors with factory equipment located in deep pockets, underground levels and other inaccessible places.
  • Flexible: NB-IoT is based on Mobile wireless technology that offers more flexibility and reduced deployment cost as compared to a wired connection.
  • Optimized for low Power consumption: NB-IoT is a 3GPP-standardised low-power wide area technology.
  • Optimized for small data rate: NB-IoT technology has an optimised data transfer rate making it suitable for applications that require reliable transfer of small, intermittent blocks of data.
  • Secure and reliable: It supports a host of security features.

Predictive Maintenance involves techniques to pre-determine an equipment fault or potential problem, that could over a period of time reduce the efficiency or cause damage to assets.

A Predictive maintenance solution leverages the data, aggregated by a number of IoT sensors, and performs an in-depth data analysis to predict any anomaly in the functioning of the critical equipment.

One of the main advantages of the Predictive maintenance model is that it performs non-interference monitoring and maintenance of the equipment. This minimizes the machine/ production downtimes, which are otherwise one of the major contributors of high operational costs

There are various types of predictive maintenance solutions. Here are three broad categories:

  • Preventive maintenance – Such systems continuously monitor the assets and try to prevent the possibility of equipment failure. Past history of failures is considered, and based on that, the likelihood and time of the next occurrence is predicted. Timely measures are then taken to mitigate such an event.
  • Risk-based maintenance – Risk-based maintenance handles systems that are highly sensitive to risks. The system analyses and finds the most economical way to minimise risks or prevent them. For instance, if a specific part of a machine is highly prone to damage, a cost-effective process can be employed to protect that part.
  • Condition-based maintenance – Such systems completely rely on IoT sensor data. This data reveals the condition of the asset over a period of time. In case there is a decrease in performance of the asset, maintenance activities are performed.
Predictive maintenance can be improved by continuously monitoring the solution. It is important to regularly evaluate the performance of the predictive models.
  • You could take measures to improve the reliability of data and collection methods.
  • You could also fine-tune your machine learning algorithms to improve accuracy of prediction.
The latest trend in predictive maintenance is the smart predictive maintenance solution. Such solutions go one step further and integrate multiple technologies and maintenance approaches. So, you could easily monitor a large network of connected assets with a fair bit of task automation and integration with ERP systems, CMMS, etc.
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