Roadmap to Services by Process Data Insights, LLC
Process Data Insights provides services and solutions to help businesses to increase profits,
improve product quality, reduce downtime, increase product&process understanding and optimize
their manufacturing or business processes. We can solve problems, e.g. excessive variation,
poor quality, defects, disturbances, equipment failure, and sub-optimal operation.
1. GET THE DATA
Client data can come from many different sources and in many different formats.
Sensors are used to measure physical properties of the process or product, e.g. temperature,
speed, vibration, distance, pressure, voltage, dimensions, etc. These physical measurements
are converted into electrical signals for data acquisition. Data is preprocessed as needed,
and saved on client PCs.
Clients will be given temporary authenticated access to a designated AWS S3 (Amazon Web Services
Simple System Storage) data storage to allow them securely to upload (copy) their data files
to Process Data Insight VPC (Virtual Private Cloud). They will use the AWS console to make
the upload. They and PDI employees will have exclusive access to the data, and will be able to
upload additional files, replace existing, or delete files. You can store virtually any kind of
data in any format on AWS S3, e.g. common alphanumeric (ASCII) *.csv and *.txt files.
Data can also be selected and read from client on-premise SQL databases into the Amazon RDS
(Relational Database Service) databases using SQL (Structured Query Language).
Streaming data is generated continuously by multiple data sources typically simultaneously. Data
needs to be processed sequentially and incrementally over sliding time windows. A wide variety
of analytics, including correlations and statistical aggregations are applied to data to derive
information. Data also be input to predictive models. Amazon Kinesis software can be used to collect
and process streaming data.
2. ANALYZE AND VISUALIZE THE DATA
We work collaboratively with our clients to analyze the data and generate actionable process knowledge
We use this knowledge to suggest practical process improvements.
Usually raw data is not ready for analytics. For best results, we preprocess it first.
That can include removing noise and outliers, scaling, time-synchronization, encoding, and feature
selection. This is often the most important and time-consuming step in the process as the analysis
results are only as good as data quality is. For best modeling results, we ensure that we find an
optional representation of your data.
Our advanced visualization tools allow us to review and share many different graphical views of
the process data. This can quickly make hidden important information visible and easy-to-understand.
Especially when there are a lot of data variables, visualization is a must
Based on our experience we have found it is important to calculate all the necessary statistical properties
of data to learn and draw conclusions. We also use Advanced methods like regression analysis, classification,
PCA (Principal Component Analysis) and ICA (Independent Component Analysis) to make more sense from the data.
Using cross-correlations we can often find sources of excessive variation and other problems in
time-series data. Autocorrelation is used to study signal similarity at different time lags for time series data.
Frequency Domain Analysis
Often data contains periodic variation indicating problems. e.g. an out-of-round roll in a continuous
process. These can be made visible and analyzed by using frequency domain tools, e.g. spectrograms,
FFT (Fast Fourier Transform) and wavelet transforms.
3. BUILD A PREDICTIVE MODEL
Process data can be used to develop models that can predict out-of-control situations or equipment
Machine learning algorithms build a mathematical model based on sample data, known as "training data",
in order to make predictions or decisions without being explicitly programmed to perform the task.
We develop accurate predictive models using machine learning methods like XGBoost, DeepAR, and Pytorch
Predicting Out-of-Control Situations
Using the models, we can predict how the process will behave, and take corrective actions as
needed. E.g. important process control handles can be adjusted to prevent process problems and
The models can predict equipment failure so that maintenance can be scheduled without unexpected
process disruptions and breakdowns. E.g. ball bearings in rolls continuous processes can gradually
4. CONSULTING AND TRAINING
Process Data Insights provides customized consulting and training in advanced data analytics and visualization,
machine learning, AWS SageMaker and other AWS solutions
Please call Esa Vilkama (763-670-1653) or email (firstname.lastname@example.org) for more
information or to schedule a meeting with us.
We can also come to you site (currently in USA and Canada) to have an all-day workshop in which we will present our capabilities,
learn your business case and process problems, review solution options, and formulate the action
plan forward. (two consultants: total $2,000 + travel cost).