Asset Failure Predictions

Reliability. Engineering.

Predictive Maintenance Solutions

Processes your IIOT sensor data in real-time to identify anomalies, raise critical alerts and trigger corrective action workflows to prevent failures.

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Condition-based Maintenance

CDLT offers Condition-based Maintenance (CBM) solutions that reduce maintenance costs and improve the asset lifetime by optimizing the maintenance schedules. This requires calculating the asset’s remaining-life based on the current and historic usage patterns and building a machine-learning (ML) model that is capable of extrapolating failures from the past to the future.

Control Inventory usage and optimization with Predictive Maintenance Solutions

Make accurate plans for inventory control by answering questions, such as:

  • Which one is the more cost-effective option: To repair the failing machines or replace them with new ones?
  • How many spare parts of type x are required to be stocked for the next 3 months?
  • What is my Economic Order Quantity (EOQ) and when is the right time to order for optimal lead-times?
  • What are the right levels of critical stock required to be maintained for the next 6 months?
Resource Planning for Predictive Maintenance

Accurate resource planning by getting answers to questions, such as:

  • What kind of skillset and labor force is required for my business continuity in the downtime?
  • When is the right time to downsize my workforce by x% this year?
  • What is the right budget for critical workforce trainings this year?
  • How many people with specific skill x is required in the workforce for the next 3 months?
  • With the current workforce size and skill set how many emergency breakdowns can we handle within SLA?
Calculate Asset ROI with Condition-based Maintenance Solutions of CDLT

Get Answers to the business questions related to the asset usage, such as:

  • What are the assets that are giving high ROI and what are the models that need to be discontinued?
  • How to maximize the asset-life without increasing the maintenance costs?
  • If I increase the inspection interval how would it impact the service and how much cost it saves?
  • What is the right maintenance budget for my assets this year ?
Predict Asset Breakdowns with Condition-based Maintenance Solutions of CDLT

Get answers to the business questions related to machine failures, such as:

  • How many machines of type x are expected to fail in the next 6 months?
  • How much down-time is expected for a specific service provided by a specific machine type?
  • Given the options to choose between two models, which one is right fit for my business continuity?
  • How to design the right Warranty plan for our manufactured product?

Real-time streaming analytics

Advanced analytics algorithms run on historical journey data collected from vehicles through On-board Diagnostic (OBD) sensors and GPS utilities give accurate information on the vehicle speeding patterns, Mileage, breaking patterns, Gear positions and regular treading patterns. This information helps in accurately profiling the nature of the driver and condition of the vehicle, and aids in designing the warranty schemes and predictive maintenance schedules.

FAILURE aNALYSIS

Risk Estimation

Estimate the total units at risk in the near future based on the historical patterns based on your current asset utilization age and the asset count.

Condition-based maintenance not only takes advantage of previous historical failure-data to predict future failures, it also can utilize the current real-time running data collected from sensors (on board the asset or vehicle) on the fly to estimate the condition of the asset and identify any potential failures, long before they can happen.

CDLT IOT platform allows you to collect the M2M Telemetry data in real-time from your assets and vehicles and identify anomalies in their behavior. Our rich array of sensor devices framework allows you collect data from variety of sources using a vast range of protocols ranging from basic HTTP REST to COAP, MQTT and ZMQ.

Automotive sensor real-time by CDLT
Predict Vehicle Breakdowns with CDLT Analytics

Some of the questions that our Predictive Maintenance (PdM) solutions help you answer are:

  • How many failures are expected by 400,000 hours?
  • Given multiple designs, which design is more reliable up to 600,000 cycles?
  • Estimating a utilization rate of 20 hours per day, how many components will fail in the next one year?
  • What is the optimal inspection interval that reduces the chance of failure?

Contact us

Book a free consulting session with us to know how your business can gather and process real-time IIOT data and take automated corrective actions to prevent the failures and increase the utilization.