Machine Learning

Machine learning algorithms have the potential to increase predictive accuracy at every stage of production.

Our world-class data science team utilise artificial intelligence and machine learning to make sense of your data and improve key performance indicators.

Machine learning is driving a revolution; allowing organisations to meet complex manufacturing challenges. From accelerating supply chain operating efficiency to creating new customised services and built-to-order on-time products; machine learning algorithms have the potential to increase predictive accuracy at every stage of production.

Many algorithms being developed are designed to learn continually, iterating in seconds, and enabling manufacturers to achieve optimal outcomes in minutes rather than months.

It can be utilised when there is a statistical relationship between historical and future data. It has been applied in many applications including sample classification, trend prediction and solution optimisation. This extremely powerful tool can be implemented to atransformative effect:

• Improve preventative maintenance, repair and overhaul performance with greater predictive accuracy at the component and part-level.
• Enable condition monitoring processes that provide the scale to manage overall equipment effectiveness at the plant level.
• Accelerate innovation and new product and service development.
• Increase production yields by optimising team, machine, supplier and customer requirements.
• Optimise supply chains and create greater economies of scale.

We are able to develop a wide range of machine learning processes and models that can provide greater insight and predictive accuracy to accelerate product development and optimise performance. Algorithms can be deployed as part of an integration with an existing system, as a standalone application or within a LabVIEW environment.

We can provide:

Supervised learning consists of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using this set of variables, a function that maps inputs to desired outputs is generated. The training process continues until the model achieves the desired level of accuracy on the training data. Example methods used for Supervised Learning: linear regression, Decision Trees, Random Forests, KNNs, Logistic Regression etc.

In unsupervised learning, there is not a target or outcome variable to predict/estimate. It is used for clustering populations in different groups, which is widely used for segmenting data into different groups for specific intervention. Examples of Unsupervised Learning:
Apriori algorithm, K-means.

In reinforcement learning algorithms are trained to make specific decisions. The algorithm is exposed to an environment where it trains itself continually using trial and error. It learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Examples of Reinforcement Learning methods:
Markov Decision Processes, Q-Learning

Our team can support you with a wide range of algorithms and languages that can be applied to almost any data problem.

• Logistic Regression
• Decision Tree
• SVM
• Naive Bayes
• KNN
• K-Means
• Dimensionality Reduction Algorithms
• Gradient Boosting algorithms
  • GBM
  • XGBoost
  • LightGBM
  • Cat Boost

• Python
• Java
• R
• C++
• C
• Scala
• Julia
• LabVIEW
• MATLAB

Our structured approach ensures that you achieve the full potential of your machine learning project.

• Requirements capture
• Exploratory Data Appraisal
• Data integration requirements

• Algorithm design
• Software architecture: dedicated stand-alone app and/or integration with LabVIEW/client systems

• Algorithm development
• System software integration
• User/Admin training

• Ongoing maintenance and support, as required.

• Gain new insights and optimise equipment performance.
• Accelerate innovation and new product or service development.
• Independent expert review of requirements.
• Effective budget management via outsourced expertise and transparent project management.
• Improve preventative maintenance, repair and overhaul performance with greater predictive accuracy at the component and part-level.
• Enable condition monitoring processes to manage equipment effectiveness.
• Increase reliability, availability & maintainability
• Prevent outages, reduce maintenance and energy-related costs
• Improve the safety of personnel & assets.
• Improve the value of existing enterprise asset management, m aintenance and product development systems .
• Increase production yields by optimising team, machine, supplier and customer requirements.
• Optimise supply chains and create greater economies of scale.

• Our consultants are experts in software integration, with a proven track record of delivering solutions in product development environments.
• Our services can be outsourced or we are happy to undertake projects…