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Cenacle Research offers Condition-based Maintenance solutions that reduce maintenance costs and improve asset life-time by optimizing the maintenance schedules. This requires calculating the asset's reamining-life based on the current and historic usage patterns and building a mathematical model that is capable of extrapolating failures from the past to the future.

It is well-known, in the field of *structurual design* and *reliability analysis*, that a component or structure designed to withstand a particular static load may fail sooner than expected under a dynamic load, which, unfortunately, is the case with majority of the manufactured assets, ranging from small scale automobiles, electronic components to large scale aerospace and nautical carriers.
It is difficult and fast becomes infeasible for a designer to consider all possible dynamic loads into the calculations right at the time of design. Rather one designs with a static-load objective with scope left for some dynamic variation that is understood through a series of stress tests and operational data gathered from real-time usage. Such data usually comprises measurements of fatigue life (or failure ages) subjected to dynamic load at varied levels.

The goal of the designer, in analyzing such failure-age data, is to select a probabilistic model, from among several reasonable alternatives, that best describes the deviation of the resulting measured values of the asset-life. If a realistic model for the probability distribution of observed lifetime is found, then it will be used to estimate the time by which the part or asset needs to be replaced to guarantee that the probability of failure does not exceed a set maximum acceptable value and meet the downtime SLAs.
One of the probability distribution models that is widely used in the study of reliability engineering and failure analysis is, Weibull distribution, as it closely mimics the characteristics of failing assets at various life-stages, and is versatile enough to mimic the characteristics of other types of distributions, based on the value of the shape parameter.

Try our Condition-based Maintenance SaaS (Software-as-a-Service) below. Start by entering the failure-ages of your observed population (you can copy|paste directly from *Microsoft Excel*) and see the corresponding *Weibull Plot* generated for that data.

If your analysis assumes that the data is following a *Weibull Distribution* indeed, then it is important to verify that assumption, and the above Weibull plot helps you exactly with that. It has special axes that are designed so that if the data do infact follow a Weibull distribution, then the plotted points will be nearly linear on the plotted chart. *Note* that the above demonstration does not take censored data into account.

One of the important contributions of this failure probability plots is, it helps you answer questions such as: what is the age at which x% of the assets will fail?

X | Design 1 | Design 2 | Design 3 |
---|---|---|---|

10 | |||

36.8 | |||

63.2 | |||

90 |

Our Condition-based Maintenance solution platform allows you to compare more designs side-by-side and evaluate custom BLife values on the fly to arrive at accurate design decisions and *what-if* analysis.

Some of the questions that our Condition-based Maintenance solution platform helps 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 an 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?

Make informed decisions with right answers

Get Answers to:

- What are the assets that are giving high
**RIO**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 ?

- 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 is the right levels of
**critical stock**required to be maintained for the next 6 months?

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

- 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*are 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**?

Cenacle Research Condition-based Maintenance solution allows you to estimate the total units at risk in the near future based on the above historical failure data. In the below data-sheet you can enter your current asset utilization age and count to get started.

Estimating an utilization rate of 20 hours per day per asset, in the next 1 year, you can expect out of your total assets to fail (for Design 1). Contrast it with other designs below.

Design 1 | Design 2 | Design 3 |
---|---|---|

This risk estimation demo does not take **inspections** into account. Our platform allows you to take inspections into account in the full version, which can considerably change the costs associated with the maintenance as well as the risk estimations. Get in touch with us to know more about it.

Cenacle Research offers Condition-based Maintenance solutions as SaaS (software-as-a-service) platform that you can readily integrate with your existing M2M and IOT solutions offerings, enabling failure prediction, risk estimation and inspection-interval optimization etc. features in no time.

IOT + M2M Telemetry + Predictive Analytics

Condition-based maintenance not only takes advantage of previous histrocial 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.
Our **IOT platform** allows you to collect the M2M Telemetry data in realtime from your assets and vehicles, and identify anamolies 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.

Cenacle Research IOT platform processes your **sensor data** in real-time to identify anamolies, raise critical alerts and trigger any **corrective action** work-flows (such as notifying the ground crew for emergency assistance on landing, priority re-routing of inbound vehicles on secondary routes etc.) . Get in touch with us to know how our IOT platform can help your business gather and process realtime data, and take automated corrective actions where necessary.