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Month: August 2019

Blog Use Cases

Predictive Maintenance in the Manufacturing Industry

 

The increase in the use of Machine Learning (ML) and in the levels of automation in manufacturing has naturally led to improving asset productivity through data-driven insights that were not possible before. Now we see predictive maintenance taking the place of the “run to failure” approach where asset maintenance is scheduled once a failure in the system is observed. Predictive maintenance is a set of processes that reduce maintenance costs and prevent extra time being wasted on repairing assets – particularly in dependent machinery systems.

In order to detect changes in the system that point to potential “health problems” in machinery, pattern-recognising machine learning models are deployed. Manufacturing companies are using predictive maintenance software to reduce unplanned downtime and maximise profitability. Machine learning predictive maintenance is unique because it not only predicts impending failure but produces outcome-focused recommendations for operations and maintenance from analytics.

 

How Does This Work?

The effectiveness of ML predictive maintenance models relies on the health indicators of the machinery. Historical data from these health indicators (features) is analysed by ML models. The ML models then compute another indicator which describes the machinery’s health and an estimated remaining lifetime.

 

The below are the base components for predictive maintenance to be carried out:

Data Sensors – sensors that collect data are installed on the machinery

Data Flow – a communication system allows machinery data to flow to a data storage centre

Data Storage – a central hub stores, processes, and analyses the machinery data either on the premises or on the cloud

Predictive Analytics – algorithms are applied to the processed data, patterns are recognised, and insights in the form of dashboards and alerts are created

Cause Analysis – corrective action is determined based on machinery health insights

 

 

When is Predictive Maintenance Being Used?

Manufacturing companies (and many other companies across different industries) use ML predictive maintenance in a number of ways. Manufacturers of machines and machinery parts use predictive maintenance to analyse and monitor the condition of motors and their levels of wear, power consumption, and productivity. Predictive maintenance is also being used to minimise production errors and reduce waste. ML models can predict when the number of faulty products is likely to exceed a set maximum percentage and give causes for the expected problems with the products. Manufacturers are also using ML predictive maintenance models to predict issues across the factory floor: by linking all monitored sections with installed machine sensors, security cameras, worker equipment, and workstations.

 

Final Notes

Manufacturing organisations who have already implemented ML into their daily functioning are seeing great benefits of predictive maintenance – an increase in information delivery speed, an increase in usefulness of information, and decreases in costs. The ML predictive analysis has allowed organisations to make smart decisions based on their own data. This results in a complete solution across operations, engineering, and maintenance. Machine learning predictive maintenance plans give managers unmatched insights into operations that can lead to optimised safety, efficiency, and decision-making. Taking a predictive maintenance approach to asset performance and reliability means manual root cause analyses are no longer needed; instead ML models will consider an entire history of asset performance and failures to identify signs of impending problems in advance.

Contact Intellify, the AWS Partner of the Year 2019 in Data, Analytics, & Machine Learning, to learn more about how your organisation can benefit from machine learning & artificial intelligence. We provide end-to-end services, helping companies reach their full potential by leveraging data proactively.

Blog Use Cases

Implementing Machine Learning in the Energy Industry

 

 

Artificial Intelligence (AI) and Machine Learning (ML) are both hot topics in business these days. These advanced analytics tools have been introduced into businesses across many sectors – from retail, to finance, to marketing, with one of the less publicised yet important sectors being the energy and utilities sector. 

 

The power of Machine Learning, data science, AI, and predictive models has made many renewable energy companies more efficient across business functions – lowering costs, increasing returns, and making more accurate predictions. 

 

Terminology 

Before getting in to how Artificial Intelligence and Machine Learning can benefit energy companies, allow the below section to provide more clarity on the terms.   

Artificial Intelligence (AI) refers to any machine or model that can conduct human-like activities.  

Machine Learning (ML) is related to AI, however they are certainly not the same thing. 

AI refers to computer-generated intelligence in machines; Machine Learning allows for some sort of artificial intelligence by using statistical techniques.  In other words, Machine Learning algorithms allow a machine to train itself based on datasets to perform a task more efficiently. 

 

Artificial Intelligence and Machine Learning in the Energy Sector 

Below are 3 use cases of these data analytics tools in the energy industry:  

 

Forecasting Energy Demand  

When it comes to delivering electricity to customers through complex power networks (grids), there needs to be enough electricity or energy available to match the demand for it. Otherwise blackouts and system failures may occur. 

 

Machine Learning algorithms can determine demand by looking at past data of daily energy consumption changes for individual customers over time. From this data Machine Learning models are able to generate very accurate energy demand and consumption forecasts. These predictions can be used by energy & utilities companies to save time, optimise operations and every storage systems, and prevent customer dissatisfaction.  

 

Predictive Maintenance for Power Systems 

In addition to matching energy demand with energy production, Machine Learning is a beneficial tool to ensure the consistency and health of power grids. In the past, massive blackouts and power cuts have occurred with no warning to energy companies of upcoming predicted faults in their systems or even warning about the power cuts currently occurring. Machine learning algorithms can implement predictive maintenance to predict when assets (power lines, stations, and machinery) are estimated to fail. These predictions are made from analysing data – collected from sensors on power lines, boxes, and machinery – against a timestamp.  This helps ascertain when assets are predicted to fail and can be used to predict the expected remaining useful life of assets. 

 

This is useful information for energy companies for saving time (replacing or fixing those assets predicted to fail before they are completely broken and have to re-ordered/replaced etc.), saving costs (allowing companies to optimise maintenance activities based on accurate predictions), and reducing customer dissatisfaction (fewer unexpected asset failures should result in more consistent running and provision of power). 

 

 

Efficient Energy Storage  

Besides being beneficial for traditional coal and gas energy companies, renewable energy companies can too reap the benefits of Machine Learning tools. A commonly known issue with renewable energy is its sporadic availability. When there is no wind or sunshine for a while and therefore not enough energy production or, on the other hand, too much energy produced for the amount of consumption (causing the surplus energy to go to waste), it is essential for energy companies to optimise energy usage and storage.  

Machine Learning solutions can be used to predict energy usage and manage storage decisions. For example, ML models can manage electricity shortages by briefly cutting off the supply for electricity coming from the main grid, while using stores of energy from regions. This reduces unused and wasted energy supplies. 

In the case of saving and storing energy, Machine Learning can generate forecasts for weather, energy production, and electricity demand to optimise energy usages on “overly productive energy days” and thus reducing the need for cutting off main grid supply.  

 

The Future of Machine Learning and Artificial Intelligence in the Energy Industry 

For developed countries with “green” goals for an environmentally-friendly economy with resistant and consistent power supply is of top priority, Machine Learning algorithms that predict demand, improve performance, reduce costs, and prevent system failures are a clear step in the right direction.  

 

With the right partner, your energy company can reap the benefits of Machine Learning and get accurate, insightful, and actionable information out of your data to more accurately forecast energy demand, predict asset maintenance, and more efficiently store energy. Save resources, time, and costs – contact Intellify to hear how we have worked within the energy industry to do just that. We can help you achieve your Machine Learning and Artificial Intelligence requirements.