Predictive Maintenance Solutions and Analytics
In Embitel’s IoT implementation journey of more than 14 years, our IoT Consultants have delivered industrial, enterprise and automotive predictive maintenance solutions for customers across India, US and Europe.
Embitel’s successful PdM deployments encompass the following:
- Industry 4.0 – Solar Energy tracking system, Industrial drive controls
- Enterprise – Enterprise Battery Management Systems (BMS)
- Automotive – Predictive Maintenance for vehicle parts
The business benefits that can be derived from our IoT predictive maintenance solutions include:
- Higher asset availability
- Improved workforce productivity
- Optimized energy consumption
- Lower operational costs
- A Fool-proof Industrial 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:
- We assist in 24/7 predictive monitoring of your field-deployed industrial and enterprise assets with the help of a well-designed network of IoT sensors. This ensures reduced reaction time to faults and instances of unplanned downtimes.
- Based on your Enterprise Asset Management requirements, the monitoring activities and collection of data can be configured as time-based or trigger-based (occurs when a specific event takes place).
- We help in Dataset preparation by refining the collected data. This data filtering ensures that only the relevant set of enterprise data is used for further processing.
- With integrated advanced Artificial Intelligence (AI) data analytics tools, enable your Industrial Asset maintenance and support teams to make more accurate and intelligent decisions.
- These AI tools are used to gain insights from volumes of IoT sensor data, which are crucial to make critical Predictive Maintenance (PdM) decisions.
- An occurrence of defect or failure may not be a one-day event. This might have been caused due to changes in operating conditions or state of an industrial asset over a period of time.
- Predictive Monitoring solutions use historic data including error logs, failed as well as successful outcomes and warnings associated with an industrial equipment – as data records.
- Our Predictive Maintenance (PdM) solution analyses & processes these data records leveraging Machine Learning (ML) techniques. This helps in detecting any anomalous equipment behaviour and thereby predicting its possible failure based on data insights.
- Real-time Industrial IoT data is represented in a visual and graphical format for an enhanced end-user experience.
- With its operator-centric HMI, our 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.
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
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):
How our Team Delivered a Predictive Maintenance Solution for UPS Battery Monitoring System
- Our customer is a trusted Tier-I supplier of electric and automation systems for Industrial Plants. On one of their Uninterrupted Power Supply (UPS) field deployment tests, they found a critical issue related to timely maintenance.
- In the absence of an IoT solution for battery monitoring, our customer could not deliver the advantages of predictive maintenance to their clients. Thus, they were looking for an IoT software development partner for custom-development of an advanced Industrial automation solution.
- Zero system downtime due to Predictive Maintenance (PdM) of the in-service UPS
- Reduced the overall cost of ownership for the client
- Enabled their system to efficiently address load balance challenges during the charge and discharge cycles
- ZigBee, EnOcean, Bluetooth and Wifi protocols/technologies
- Design, development and integration of cloud based SaaS
- iOS/Android App and a web dashboard that delivered a ‘delightful user experience’ to administrators through intuitive UI/UX design
- Tensorflow: Machine Learning Library for differentiable programming and deep learning
- Microsoft Azure Blob Storage: for reliable storage of data
- Python Script: data extraction and data cleansing
System Design of our IoT Predictive Maintenance Solutions
- Sensor network for Data Collection:A powerful network of IoT sensor nodes are integrated with the industrial assets to constantly monitor their conditions. 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 Cloud backend.
- Data is filtered to identify relevant information from raw data.
- 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 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.
Following are the primary components of our predictive maintenance solutions:
Meet Our IoT Leaders
[IoT Video] How Does Predictive Maintenance Solutions Work?
Related Blogs: Learn more about Predictive Maintenance Solutions, Future Trends of Industrial Asset Management & more
- Does the Future of Industrial Asset Management Belong to Predictive Maintenance?
- What is Predictive Maintenance (PdM)? Learn How Industrial IoT Enables PdM
- Predictive Maintenance case-studies from Railway, Energy, Oil & Minerals Industries: The Challenges and Benefits
- [Vlog] How Does a Predictive Maintenance (PdM) Solution Work?