Predictive Maintenance &
Maintain your Industrial Machinery at Optimal Health
Predictive Maintenance and Real-time Monitoring
Machines, equipment, and technology are an integral part of business operations. Construction, Energy, Manufacturing, Aviation, Railways, and Shipping industries have heavy industrial equipment that run round the clock to keep the businesses running. As much as businesses depend on them, machines are prone to wear and tear. To address the issue of maintenance, businesses traditionally took reactive measures rather than preventive measures.
Traditional maintenance processes react to the failure of a component, but do not have a mechanism to predict and prevent asset failure altogether. With Reactive Maintenance, companies lose the effectiveness of expensive machinery and incur the costs of unplanned downtime. With Predictive Maintenance, real-time health reports collected by IoT/IIoT sensors trigger the maintenance process, much before the point of failure.
- Scheduled monitoring (weekly or monthly) of industrial equipment by technicians or engineers
- Scheduling of maintenance and procurement processes only when some machine breaks down
- Fixing the wear and tear after the point of failure
- Long periods of unplanned downtime
- Unplanned hit in production/service delivery
- Real-time monitoring of industrial equipment performance using IoT, IIoT and Artificial Intelligence
- Automated scheduling of maintenance processes and procurement requests for parts
- Fixing the wear and tear before the point of failure of the asset
- Reduced chances of downtime by 50%
- 40% increase in production/service delivery
Experience a radical decrease in the frequency and amount of unplanned downtimes caused by contingent situations due to technical failures.
Implementation of predictive maintenance can cut maintenance costs by 40%
Enquete Group’s Predictive Model of Maintenance
Enquete Group’s Predictive Maintenance model estimates the time up to an industrial asset’s failure by using data obtained by IoT/IIoT sensors from the asset itself. IoT/IIoT sensors acquire data from the industrial equipment continuously. AI/Machine Learning identifies patterns within the function of a machine. Statistical algorithms analyze the findings to detect the correlation between patterns, exceptions to those patterns and equipment performance.
We provide a highly agile platform that designs the choice architecture on the basis of the knowledge of trained technicians. Using this architecture, the equipment’s performance can be evaluated in real-time. In this way, the point of failure of equipment can be predicted and follow-up actions on maintenance can be prioritized as per the severity of a problem.
Perfect solution for Large-scale battery-driven powerhouses, Housing Boards, Data Centers.
Root Cause Analysis & Process Failure Analysis
Visually Charting of the Operational Efficiency
Optimal Spare-part & Resource Inventory Utilization
Integration with Existing ERP Systems
Quality Assurance in Production/Service Delivery
Meet our Team of Data Scientists
Some of Enquete’s Data Collection Points
Our Predictive Maintenance platform gathers and analyzes equipment health reports from some common data points
using our best performing techniques and procedures
Machinery failure tends to be triggered by components that are mechanical and prone to issues like imbalance in fitting, non-alignment, greasing issues. Through monitoring the changes in the behavior of such components using the sensors, vibration profile can be created. Maintenance technicians can detect variation in the vibration profile of the components in real-time and can schedule maintenance before the point of failure. This invaluable database of equipment profile enables machine health monitoring using vibration analysis.
Modern-day electrical equipment boasts of high reliability but to make places with high chances of poor ventilation like factories, manufacturing plants, office complexes, etc safer, predictive maintenance is required. Thermal imaging and infrared techniques gather data regarding the level of heat absorbed or released by the electrical equipment on a real-time basis and formulate a range of attention-seeking hotspots for maintenance to restrict unplanned downtime of machinery.
Equipment depend upon the functions of their mechanical parts. Their bearings and chains are supported by industrial standard lubricants to help them work seamlessly. Oil analysis as a tool for predictive maintenance that decreases the risk of uncertain failures by increasing the reliability of the components. It also helps to detect potential failures when there is contamination in the lubricants. This data on fluid property and possible contamination enable the maintenance technician to have a detailed view of what exactly is going on inside the machinery.
Businesses that could benefit from
Enquete Group’s Predictive Model of Maintenance
Industries that have adopted the Predictive Model of Maintenance and are yielding results
Power and Energy
Northern American Energy Companies have optimized operating costs, increased profitability, and gained a market share of 31.67% in a year just by raising the predictive maintenance efforts by 24.5%.
Predictive Analytics can assess the performance of assets such as a battery or a cell, based on its charging cycle, identifying data points such as heat, pressure, vibration, etc. This enables the power industry to measure the temperature & voltage performance for each of the cells of the batteries. Advanced analytics provides charging cost predictions for large powerhouse batteries; it supports engineers and process managers in the proper management of available resources. It can also automate the process of replacing the batteries, whenever required. Predictive Maintenance of batteries has a direct impact on power generation cost, customer satisfaction & high availability of the supply.
Organizations have manpower but they also have a dependency on IT infrastructure to continue their operations. Using Data analytics to scramble big data, patterns of failure of IT equipment are determined and they are repaired or replaced before unnecessary downtime occurs. Predictive maintenance comes into play again.
Unplanned downtime and asset failures can be disastrous for manufacturing businesses. A study in the USA suggest that manufacturers lose $22000 by the minute due to machine failures. This creates a roadblock in supply chain management. Keeping the production running efficiently is an exhaustive process, especially when the process failure is unprecedented. With the application of predictive maintenance, the failures can be reduced by 5-50%.
The Enquete Group Advantage
- Maintain consistency in your operations as our data-driven maintenance strategy enables you to know what is likely to happen inside the complex machinery. Data is continuously fed into the model to get the desired outcome and accuracy of prediction.
- Reduce unplanned downtime and unexpected equipment failures. Free up capital by reducing unnecessary spare parts in the inventory. Enhance performance of your industrial machinery and thereby reduce the chances of slow-down.
- Reduce the cost and time of maintenance with the help of our Predictive Maintenance model. Invest your valuable business hours in exploring growth opportunities.
- Reduce operating costs to create an impact on your profitability immensely. According to our analysis, the application of predictive maintenance could save you anything between $240- $630 Billion by 2025.
- Avert the risk of losing lives due to the failure of equipment by early detection. Major industrial catastrophes can be avoided by preemptive actions on early warnings.
- Enhance the quality, customer satisfaction, and reliability factors in your products and services.
- Seamlessly integrate your Enterprise Resource Planning tool with the Predictive Maintenance platform for automated resource management.
- Our Team of Data Scientists is dedicated to gather your data and transform it into actionable insights so that your business can leverage the insights to ensure optimal health of your industrial assets and machines