Preventive maintenance is a routine maintenance process on a piece of equipment to lessen the likelihood of a sudden breakdown
Preventive maintenance is to maximize the useful life of any particular asset.
The main challenge with preventive maintenance is balancing the cost with returns. Experienced maintenance managers must make smart decisions on machines which requires preventive maintenance work and its frequency ..
Production stoppages cost money. The longer the stoppage , greater the revenue loss and operating cost.
Studies show that unplanned downtime costs manufacturers an estimated $50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime.Maintenance means maintaining something by understanding the internal and external factors. As sudden machinery breakdowns can lead to downtime and expensive reparation.Top priorities is to maximize the machinery life cycle.
Transformation to Smart Maintenance /Predictive Maintenance (PdM)
Next generation solution for maintenance with advanced analytics is rightly serving for Predictive maintenance
Artificial Intelligence(AI) takes the tasks of Prediction and provides advanced analytics, leaving humans to operate at a higher and strategic level.
The data revolution of the manufacturing industry, Industry 4.0, has enabled a multitude of data driven processes to thrive, creating today’s Smart Maintenance.
Smart maintenance helps to predict malfunctions and problems in order to act before real failures occur and problems persist.
As a result of predictive maintenance, production times and the service life of the machines used can be extended
Three important steps to use Predictive maintenance
- The collection in real time
- Storage, analysis and evaluation of the collected data sets
- Collected data will be checked with normality and abnormality conditioned with defined intervals or at various scripted events with established baselines.
The goal with preventive maintenance is to catch breakdowns probabilities by monitoring equipment conditions.
A large concern for manufacturers is downtime. While IoT and Machine learning algorithms connectivity are helping predict and detect problems before they occur.
Machine learning (ML) is one of the ways we expect to achieve AI. Building a system that automatically improves with experience with fundamental laws that govern all learning processes is machine learning.Machine learning relies on working with small to large datasets by examining and comparing the data to find common patterns and explore nuances.
Let’s take a case — A Cutting tool in a machine is to be monitored
A vibration change means the cutting tool needs to be replaced or sharpened soon.
It can be performed by means of setting benchmarks and its behavior at various stages for different jobs with Machine learning , which uses massive amounts of structured and semi-structured data to generate accurate results or give predictions based on that data.
Predicting a tool or equipment are likely to fail and recommending optimal times to conduct maintenance (condition-based maintenance) is the most popular use case of AI in manufacturing today.
It can be done with an effective method — Failure Mode and Effects Analysis (FMEA).
Such Analytical can be various probable means using AI by integrating below .
- Mastering the tool or process and list their normal functions.
- Bench marking its limits on set of data s
- Failure conditions of the asset or tool.
- Ranking the severity of each failure
- Determine the occurrence of each failure mode.
- Action on failure modes
- Frequency of occurrence to be analysed
- Stamping Abnormal data s and its instance and frequency for behavior of the machine .
The most common condition monitoring techniques used to detect these faults with
Smart Maintenance are:
- Vibration analysis
- Lubricant analysis
- Pressure analysis
- Ultrasound testing
- Acoustics testing
- Current and voltage testing
- Thermal cameras
- Leak detectors
Smart Maintenance will help to build Smart Factories 4.0 with ML with AI and will
- Increase productivity
- Reduce maintenance costs
- Extend equipment lifetime
- Ensure compliance & Enhance safety
How ever ,AI adoption seems to be moving slowly despite early successful
AI is still a new technology. This generally requires more connectivity and large data to maintain accuracy Much of the success has been in the form of test-beds, not full-scale projects.
Many companies have legacy equipment that does not provide data or a way to send data to another location.
For an example, some factories might lack easy access to smart sensors or an IT network to get the data where it can offer greater benefit.
However once AI gets adopted , Maturity, scalability , predictability will increase ROI.
Naraiuran — Smart Asset care — New way to improve productivity.
Naraiuran will innovate the ways to increase value for our existing customers exploring new businesses by embedding ourselves in new market trends to capture opportunities outside our traditional business.
- Control & Automation
- Handling system — Robotics
- Plant Integration
- Cloud Integration &
- IoT for tracking system and monitoring
“Our solutions deliver intelligence, control , optimization with seamless integration.”