Business Value-adds of our Predictive Monitoring Solutions
Our IoT Consultants for Industrial Automation have delivered projects related to Enterprise Battery Management Systems (BMS), Solar Energy Tracking System, Industrial Drive Controls and more.
In this journey of more than 13 years, the following benefits have been delivered to customers across India, US, Germany and Europe:
- Higher Asset Availability
- Improved Work-force Productivity
- Optimized Energy Consumption
- Lower operational costs
- A Fool-proof Industrial Asset Management solution at your disposal
Features of our Cloud Based Predictive Maintenance Solution:
Designed based on Internet of Things (IoT) Technology Stack, our Predictive Monitoring, Analytics and SCADA solutions are designed for Industrial Asset Management.
Here is a sneak-peek into some of the carefully designed features that ensure a winning RoI:
- 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 requirement, this monitoring (collection of data) can be configured as either time-based or trigger-based (occurs at a specific event).
- Dataset preparation by refining the collected data. This data filtering ensures that only 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 an 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 Monitoring system uses historic data including error logs, failed as well as successful outcomes, 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 the 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, 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.
Meet Our IoT Leaders
Suhas has over 25 years of experience in Embedded Engineering & Software Development. He is well-known, among his peers and customers, for his ability to ensure timely delivery of IoT projects. He has been instrumental in the successful completion of some very challenging and large scale IoT projects at EmbitelSuhas Tanawade, Senior Delivery and Account Manger, IoT
[IoT Video] How does a Predictive Maintenance (PdM) Solution Work?
System Design of our Predictive Maintenance Solution
- Following are the primary components of our predictive maintenance solution:
- Sensor network for Data Collection: A powerful network of IoT sensor nodes is 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 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 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 data from raw data.Depending on the project requirements, predictive maintenance algorithms (Machine Learning, Deep Learning etc) can be integrated with the Cloud Application.Data is processed and analysed using Machine Learning models ( based on project requirements) and AI tools , to accurately predict equipment failureCloud 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 operationsData is made available real-time and user-role management, report generation and other plugin integrations can be customized as per the requirements.
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 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.
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.
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 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 define their industrial asset Management goals and design a customized Predictive Maintenance solution for them.
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. 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.
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. Following is a list of tools used in our Predictive Maintenance Solution :
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
Ans. The criticality of accuracy of the results delivered by Predictive Analytics varies as per the use-case.
For example, a 99% accuracy in predicting equipment failure is very critical for safety-intensive applications such as medical equipment, automotive, factory shop-floors etc.
We can help you achieve a 99% accuracy in predicting equipment failure provided we have access to high 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.
[Customer Success Story] How our Team Delivered a Predictive Maintenance Solution for UPS Battery Monitoring System
Find out how we are partnering with industry leaders to create intelligent, fool-proof industrial maintenance systems using Predictive Maintenance:
Design and development of IoT Home Automation system
- Business Challenge:
- Our customer, a trusted Tier-I supplier of electric and automation systems for Industrial Plants, on one of their Uninterrupted Power Supply (UPS) field deployment tests, found a critical issue related to timely maintenance.
- In the absence of an IoT platform solution for battery monitoring, our customer could not deliver the advantages of predictive maintenance to its clients. Thus they were looking for an IoT software development partner for advanced Industrial automation solution.
- Embitel Solution:
- Zero system downtime due to Predictive Maintenance (PdM) of the in-service UPS
- Reduced the overall cost of ownership for the clients
- Enabled their system to efficiently address load balance challenges during the charge and discharge cycles
- Tools and Technology:
- ZigBee, EnOcean, Bluetooth and Wifi protocols/technologies
- Design, development and integration of cloud based SaaS
- iOS/Android App and a web dashboard that delivers ‘delight’ to end-users through our intuitive UI/UX
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?
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): 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
What is the role of NarrowBand IoT (NBIoT) in enabling Predictive Maintenance of enterprise & industrial assets?
- 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 salient features offered by Narrow Band IoT technology.
- Higher penetration power: NB-IoT can be used to connect IoT sensors, 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: Supports a host of security features
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.