What if equipment was able to predict when it might breakdown or malfunction ahead of time?
Welcome to another aspect of becoming digital first, a comprehensive and informative process which can be harnessed to inform decision makers, schedule Inspectors and preserve downtime in a company.
The effects of predictive maintenance on a company according to Deloitte.
Preventive Maintenance
To understand the benefits of predictive maintenance lets first look at how and why the need has changed and evolved.
Equipment regardless of size or use has a shelf life and depending on the type or complexities it serves will determine how long this might remain in service as purchased. Most businesses carry out routine maintenance scheduled at specific times during this lifecycle. Regular intervals such as monthly or annual checks are conducted by resources being deployed to assess, manage components and upgrade where necessary.
Preventive maintenance puts a huge strain on company finances when equipment is rendered out of action whilst it is being fixed or in the event of a breakdown. These preventive actions whilst viewed holistically manage to correct incidents, the performance costs incurred are enormous. Plus, the output is punitive as decision makers don’t know which parts will fail at any given time. There is no specific knowledge held within the business apart from the experience of using a type of model.
This outdated process forces site managers and technicians onto the back foot manually predicting performance errors. Working blindly without complete, concise pieces of information that could be used to stop a piece of equipment from malfunctioning. The entire process is labour intensive, expensive and not scalable especially if resources are suddenly diverted to attend emergency issues.
Predictive Maintenance
Instead, predictive, reactive or condition maintenance as it is sometimes known can be used to identify any type of fault before it becomes a necessity to fix. According to Statista the global market size is expected to reach 23.5 billion U.S. dollars by 2024 up 23.5% from 2018.
Since the 1990’s, industrial sectors have started implementing predictive maintenance strategies designed to facilitate the condition of equipment in real time. The following is a summary of how this process can be useful:
- Extends lifecycle of in-service equipment
- Reduces labour costs
- Eliminates the need for large supplies of spare parts
- Enhances resource safety
- Improves overall equipment productivity
- Problems are fixed before they happen
- Reduces failures and downtime
- Condition monitoring detects equipment anomalies
- Adds ROI of value on innovations via digital transformation
An important aspect of managing maintenance and faults are the people assigned to do the repairs. If errors are predicted in advance, then resources can take extra safety precautions planning how to best solve the problem. This analysis might enable the use of Aerial Unmanned Vehicles or other robotic devices where the risk to human life is determined too severe.
In basic terms predictive maintenance becomes less of an emergency requiring perhaps a greater level of skills into an ongoing manageable exercise. This is highly beneficial to decision makers in maintaining equipment, reducing downtime and the ultimate smooth running of operational activities.
Sensory IoT
The first step to adopting predictive maintenance capabilities requires establishing an IoT infrastructure where systems can capture and read the information contained within the equipment. This process is enabled by installing sensors or digital controllers which communicates with management systems.
Equipment actions can be recorded and stored thus creating high velocity, high volume and varied types of big data which can be analysed to mitigate disaster. The data becomes a crucial asset to the business and therefore must be protected. Write once, read many (WORM) is a storage device in which data can be saved without being erased and then retrieved as many times as needed. This is just one of many storage options available.
The data captured becomes an intricate part of turning a company into a smart business with decisions being made ahead of time on which equipment should be monitored and prioritised if an expectant failure is likely. It switches a company from managing preventive maintenance into predictive maintenance saving time and money.
Using Big Data Analytics
Whilst the use of Big Data is extremely advantageous it needs to be queried before it becomes useful. To do this a company needs to apply data science methodologies such as machine learning and or artificial intelligence.
These futuristic technologies can be used to create reports generated to address specific areas of interest including the functionality, age, purpose and behaviour of the equipment. Factory workers, technician hubs and procurement teams can learn a lot from this data set to improve manufacturing operations by accurately determining which hardware kit is going to fail. And which parts will need to be sourced to fix the problem.
To be effective artificial intelligence and machine learning capabilities needs a history of data to further explore clusters of statistics searching for patterns and forming anomalies. This process can be further intellectualised as more data becomes available. The machines or in this case algorithms can learn and suggest correlations taken directly from the sensors and improve the systems intellect over time without human intervention.
Global Resources
At VERITAS we supply Inspector resources to industries who need to carry out testing, inspection and certification of equipment. We are at the forefront of this necessity presenting a revolutionary cloud-based solution to source, manage and control inspections.
For predictive maintenance to be successful a business will need a supply of skilled resources deployed to fulfil client contracts. Time and locality become a critical issue ensuring that resources are readily available and can be assigned on a need by need basis.
Our QA/QC platform allows clients to select inspectors based on cost, skills and those closest to the operational site to be deployed quickly mitigating problems ahead of failure. Once an inspector is chosen, they can download client documents to assess how best to fix equipment before it malfunctions. The client will then receive an assurance report and safety certificate to confirm work has been done in line with quality and standards.
Conclusion
The concept of predictive maintenance is yet another example of how digital transformation can be helpful to a business. This technology becomes a critical tool in a company’s arsenal and in their quest to automate parts of the business that can offer the greatest return of value.
The management of equipment is essential to ensuring that operational activities can continue without problems and the use of IoT sensors can be applied to any hardware. For this to work efficiently data captured needs to be processed quickly to form an understanding of behaviours to eradicate problems before they happen.
The sooner a company begins this collection process the more they can learn from system centric algorithms which corroborates understanding and improves over time. Thus, forecasting a logical relationship between what constitutes a good and bad service against a baseline fluctuation that can be tracked for immediate action.