It is rightly said that ignorance is a choice in the information age. And we have resorted to that choice for a brief time. Spending millions of dollars on repairment, letting our resources be natural, financial, or human drained dry, and mishandling the causal effect. However, with now bright minds conjuring ways for a brighter future, prevention better than cure is seemingly a more worldly and profitable idea.
The power industry is moving steadily towards predictive maintenance in pursuing a better future. This industry is acutely inclined on natural resources, harnessing millions in repairment derivatively each year, spending time and workforce in replacement, recycling, and chucking off machinery in a relatively short period because of rapid technological advancement. However, they can't help it because they are the ones keeping the circuit for other industries running, and therefore an automation breakdown in these industries on a fine day will lead to a massive loss in terms of time, investment, and other operations, pushing efficiency on the back seat for a long time. Predictive maintenance then becomes a virtue that cannot be ignored anymore.
In simple terms, predictive maintenance is one umbrella term for all methods of proactive maintenance that are designed to study the condition of equipment, laying down a prediction for when designated maintenance would be required. By blending data science and predictive analytics, predictive maintenance estimates when a piece of machinery might fail so that thorough rectification care can be scheduled to prevent that point of failure. This maintenance is scheduled at a time when it is convenient and cost-effective, neither too soon so that it is rendered unnecessary nor too late so that there is no time to prevent the shutdown from happening. Predictive maintenance optimizes the lifespan of the equipment to its penultimate, but that is possible only when it has not been compromised. Hence, both time and daily treatment are of the essence for predictive maintenance methodologies to be a success. This is best understood from the underlying architecture of a divested predictive maintenance, which typically constitutes:
- Data acquisition and storage
- Condition monitoring
- Data transformation
- Asset health evaluation
- Human interface layer
To investigate for any inherent problem, it incorporates methods like:
- Corona Detection
- Oil analysis
- Sound level measurements
- Thermal imaging and more
- Vibration analysis
These measurements, along with machine learning tactics like regression or classification approach, identification of equipment vulnerabilities, etc., give a clear picture of the health of the piece of equipment in the status quo.
One must understand that this maintenance modi operandi is exclusive to depreciation. The depreciation of a machine is inevitable; regular wear and tear and exhaustive application over the years cannot be negated out of the equation. However, predictive maintenance does acknowledge all of these and gives a diligent report of the machine amid this regular operation. It predicts anomalies resulting from unintended mishaps, lack of insight, and unforeseen events.
Thus, it expedites efficiency in every industry, especially the power industry. If you may ask, how? Understand this.
Each year, power industries spend around 57% of their expenditure on unplanned repairs resulting from component failures. As per the new research done by Wood Mackenzie Power and Renewables, "Unplanned failures can cost the asset owner as much as $30,000 per turbine per year in terms of repairs and spare parts and up to 7 day’s worth of lost production per year - not including production losses caused by pre-emptive shutdowns or long delivery times for materials, equipment and technicians to the affected turbine." An unplanned wind turbine repair costs as much as $8 billion! These statistics vary but are not far from each other for other power industries. When the returns are high, you must have heard so are the stakes. Therefore, predictive maintenance creates a ripple effect toward more equitable and sustainable power generation, thus changing the stakeholders' philosophy and representing a paradigm shift in how the power industry will manage its assets in the near future.