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Predictive Maintenance Solutions

Predictive Maintenance Solution for Industrial Assets

Designed and developed by an IoT team with over 11 years of Industrial Automation experience, our advanced predictive maintenance solutions offer the following benefits:

  • Higher Asset Availability
  • Improved Work-force Productivity
  • Optimized Energy Consumption
  • Lower operational costs
  • Fool-proof Industrial Asset maintenance & support system

Features of Predictive Maintenance Solution:

We have designed a highly efficient and intelligent Predictive maintenance system for your capital intensive Industrial Assets.

Here is a sneak-peek into some of the carefully designed features to ensure a winning RoI:

  • 24/7 monitoring of your industrial equipment ,with the help of a well-designed network of IoT sensors, to help in reducing the reaction time to faults and instances of unplanned downtimes.
  • Based on your industrial maintenance requirement, this monitoring (collection of data) can be configured as either time-based or trigger-based (occurs at a specific event).
  • An occurrence of defect or failure may not be a one-day event and might have been caused due to changes in operating condition or state of an industrial asset over a period of time.
  • Our Predictive Maintenance (PdM) solution leverages Machine Learning (ML) techniques to closely study the patterns and identify any deviation in the IoT Sensor data in order to predict a possible fault.
  • This pattern learning feature along with the customized reports for root cause analysis helps a maintenance team to timely identify the defect point and avoid potential downtime.
  • Real-time Industrial IoT data is represented in a visual and graphical format for an enhanced end-user experience.
  • With its operator-centric HMI, this predictive maintenance dashboard is designed to enable accurate and faster decision-making.
  • Our Industrial IoT interface can also be integrated with touch, voice and gesture based controls as per your business requirements.
  • With integrated advanced AI and data analytics tools, enable your industrial maintenance and support teams to make more accurate and intelligent decisions.
  • These AI tools are used to gain insights from volumes of sensor data, which are crucial to make critical predictive maintenance decisions.
  • Meet Our IoT Leaders


    Components of Industrial Predictive Maintenance Solution

      Predictive Maintenance Solutions

      Following are the primary components of our predictive maintenance solution:

    • Sensor network for Data Collection: A powerful network of intelligent sensors integrated with the industrial assets to constantly monitor their conditions. These sensors collect real time data regarding current health of the assets. The collected data is compared with the preset 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.

      This IoT gateway acts as a communication bridge between the sensor nodes and cloud back-end.

    • Data Processing Algorithms: Raw data from sensors is converted into actionable insights at the Cloud backend.

      Depending on the project requirements, data processing algorithms (including AI, ML and more) can be integrated with the Cloud Application

      Cloud backend also hosts databases and an interface is designed to manage integrated third party systems

    • Mobile and/or Web interface: With operator centric HMI/UI, the mobile app and/or web dashboard act as a central control unit for managing the plant operations

      Data is made available real-time and user-role management, report generation and other plugin integrations can be customized as per the requirements.


    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 to communicate 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): 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


    FAQs on Predictive Maintenance Solutions

    Q. What is the business engagement model for Predictive Maintenance solution development?

      A. 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 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 the 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)

        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.


    Q. How is the maintenance team alerted about a possible fault or hazard?

      A. 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.


    Q. In what ways can our organization leverage the Predictive Maintenance Information to gain competitive advantage?

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

      The Predictive 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


    Q. Can you share details regarding the skill-sets and expertise of the software and hardware development team behind your Predictive maintenance solutions?

      A. 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


    Q. What are the typical challenges you face during design and development of a Predictive Maintenance Solution? How have you been addressing them?

      A. One of the main challenges associated with developing a Predictive Maintenance Solution the fact that there is no universal/ one-size-fits-all predictive maintenance solution.

      Every industry facility is unique, with its own set of “specialized information” to be collected for reliable 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 gathered and to plan 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 clearly define their industrial asset maintenance goals and design a customized Predictive Maintenance solution for them.


    Q. 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?

      A. 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.

      The data analysis is performed typically on – a public or a private cloud platform.


    Q. Please share some customer success stories of your Predictive maintenance solutions?

      A. We have been partnering with various business organizations across the world, helping them efficiently manage their capital intensive Industrial Assets.

      Here is a summary of one such collaboration (Please contact us to know more details and success stories on Predictive maintenance solutions):

      • We designed IoT platform to enable predictive maintenance for UPS Battery monitoring System for a leading supplier of electric & automation systems for Industrial Plants.
      • Our IoT automation solution, helped in identifying & isolating the discharged batteries, enabling appropriate maintenance operations to be executed.

      The tailor-made design of the UPS Battery monitoring System resulted in the following positive outcomes:

      • Reduced the overall cost of ownership
      • Minimized System downtime as per the desired SLA
      • Managed the load balancing issues due to the charging and discharging cycle of the battery.


    Q. Is it possible to integrate your Predictive maintenance solutions with the legacy industrial assets and production systems?

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


    IoT in Action: Success Stories

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


    What is Predictive maintenance?

      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 the industrial assets.

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


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