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
Event/Time Based Data Collection
- We assist in 24/7 predictive monitoring of your field-deployed industrial/enterprise assets or vehicle parts with the help of a well-designed network of IoT sensors. This helps in foreseeing equipment failures and instances of unplanned downtimes.
- Based on your 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).
Integrated Data Analytics
- We help in Dataset preparation by refining the collected data. Data filtering ensures that only the relevant set of data is used for further processing.
- With integrated advanced Artificial Intelligence (AI) data analytics tools, enable your Industrial/Enterprise/Automotive Asset maintenance owner to make more accurate and intelligent decisions.
- These AI tools are used to gain insights from large volumes of IoT sensor data, which are crucial to make critical Predictive Maintenance (PdM) decisions.
- As discussed above, the occurrence of a defect or failure may not be sudden. The failure might have been caused due to changes in operating conditions or the state of an asset over a period of time.
- Our Predictive Monitoring solutions use historic data including error logs, failed as well as successful outcomes, and warnings associated with the equipment – such 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 its possible failure based on data insights.
- Real-time Industrial IoT or Connected Vehicle data is represented in a visual and graphical format for an enhanced end-user experience.
- With its operator-centric Human Machine Interface (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.
- We help you identify the right cloud solution for the expected workload.
- This way, you can benefit from the agility and synchronisation benefits of hyper scale clouds.
- Improved performance and security are guaranteed with our multi-cloud and hybrid IT services.
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:
- o Real time parameter data
- o Historical Data
- o Alarm and alerts
- o Commands
IoT Predictive Maintenance Customer Success Stories
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?
Here are the primary components of our predictive maintenance solutions:
- o 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.
- o 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.
- o 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.
- o 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.
Customer FAQs Regarding Predictive Maintenance Data, Integration with Legacy Systems and More
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
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 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.
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.
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.