Service overview
Machine learning, predictive maintenance, computer vision, sensor analytics and forecasting. This service is part of that division and is scoped individually around what you need to learn, decide or publish.
Engineering data come from test rigs, sensors, simulations, inspection records and process logs, and they bring features that generic data-science tutorials rarely address: units that change between files, time stamps in different clocks, sensors that drift or saturate, calibration events recorded in notes and measurements that are expensive and few. Data science for engineering is largely the discipline of making such data trustworthy and then asking good questions of them.
The work usually has three phases. First, preparation: aligning, cleaning and documenting the data so that every number has a traceable origin. Second, exploration: visualising and summarising to see structure, anomalies and relationships. Third, modelling and communication: turning findings into conclusions that others can use. The first phase takes the most time and has the most influence on the validity of what follows.
If you plan to publish the outcome, see manuscript & publication support, for example computational & simulation manuscripts or original research article writing.
What this service covers
Problems this service can address
- Messy data from multiple instruments and time bases
- Missing values and sensor drift
- Analyses that are not reproducible
Methods and analytical approaches
- Data cleaning, alignment and unit harmonisation
- Exploratory analysis and visualisation
- Dimensionality reduction and clustering
- Reproducible analysis workflow documentation
Applications
- Test-campaign data analysis
- Process-data investigation
- Dataset preparation for publication
Relevant engineering disciplines
Scope, inputs and deliverables
Scope of work
- Data audit and definition of the prediction or decision task
- Model development with structure-aware validation and baselines
- Evaluation, error analysis and uncertainty reporting
- Documentation sufficient for others to reproduce the work
- Feasibility check on: raw data files with format description
Typical inputs required from you
- Raw data files with format description
- Questions the data should answer
- Known quality issues
Deliverables
- Cleaned dataset with a cleaning log
- Analysis findings and figures
- Reproducible workflow description
Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.
Project workflow and method
- 01Enquiry and requirement assessment
- 02Technical scoping and proposal
- 03Execution
- 04Quality review and delivery, with an agreed round of revisions
Read the full project workflow.
In depth: how the work is done
Cleaning, alignment and unit harmonisation
Datasets from different sources must be merged on a common time base or identifier. Units and conventions, such as sign of strain, reference temperature or coordinate frame, are checked and unified. Missing values are characterised: whether they are random, due to sensor failure or systematic. Each cleaning step is recorded in code or a log so that raw data remain untouched and the processed dataset can be regenerated. This traceability is essential for publication and for responding to reviewers.
Exploratory analysis and visualisation
Good plots reveal structure that numbers hide: drift in a sensor, a change after a recalibration, clusters corresponding to operating modes, outliers due to handling errors. We use distributions, time plots, scatter matrices, heat maps and faceted views, choosing scales and annotation carefully so that conclusions are not distorted. Exploratory analysis is also used to generate hypotheses, which are labelled as such and tested separately to avoid mistaking patterns found in the same data for confirmation.
Dimensionality reduction and clustering
With many sensors or features, techniques such as principal component analysis summarise variation in fewer dimensions and reveal correlated groups. Clustering methods identify operating regimes or failure signatures. Both depend on scaling and on choices such as the number of components or clusters, which are justified and tested for stability. Results are interpreted with domain knowledge, since statistical clusters need not correspond to physical states.
Reproducible workflows and documentation
Analyses are organised as scripted workflows rather than manual spreadsheets, with a clear directory structure, recorded software environments and instructions to regenerate results from raw data. Figures and tables are produced by code. Metadata describe sensors, units, sampling and known issues. Such documentation makes it feasible for collaborators to reuse the data, for reviewers to check results and for you to return to the project months later.
Preparing datasets and findings for publication
Many journals expect data availability statements, and datasets may be published in repositories. We help describe data with sufficient metadata, choose formats and structure, flag privacy or confidentiality constraints and write the analytical description for the methods section. Findings are presented with appropriate caveats, uncertainty and limitations. Where datasets cannot be shared, a clear statement and a synthetic or reduced example may be provided.
Quality control and good practice
Quality-control approach
- Data are audited for quality, leakage and drift before modelling.
- Validation respects data structure (by specimen, machine or time) and includes simple baselines.
- Performance is reported with uncertainty and failure cases, not only headline scores.
- Model limits and the conditions under which it should not be trusted are documented.
Common pitfalls this service helps avoid
- Editing raw data by hand. Untracked manual changes destroy traceability and make results irreproducible.
- Mixing up units or sign conventions. These silent errors can produce plausible but wrong results.
- Over-interpreting clusters. Clusters are properties of the algorithm and data; they need physical confirmation.
- Exploring and confirming on the same data. Patterns found by exploration should be tested on independent data or with appropriate corrections.
What good reporting looks like
A data-science report describes the data sources and their provenance, the cleaning and harmonisation steps, quality issues found, the exploratory findings with figures, the methods used for any modelling or clustering, the results with uncertainty, the interpretation in engineering terms and the limitations. Scripts and environment details are provided to reproduce the analysis, and the processed dataset is accompanied by a data dictionary. Claims are tied to the evidence shown.
An illustrative scenario
Hypothetical example written for explanation. It is not a client case, a result or a testimonial.
Suppose a team has strain gauge, temperature and load data from a three-month test campaign, saved by different operators in different units and file layouts. Harmonising them reveals that one channel was recalibrated halfway through, creating an apparent step in the data. Exploratory plots show that a drift in a thermocouple correlates with room temperature. After correcting these, the team can analyse the true specimen behaviour, and the scripted workflow lets them regenerate every figure for the paper from the raw files.
Questions to settle before you begin
- Where did each dataset come from, and how was it recorded?
- Are units and sign conventions consistent across files?
- What known events, such as recalibrations, affected the data?
- What question should the exploration answer?
- How will I share or archive the data and code?
Key terms
- Provenance
- The record of where data came from and how they were transformed.
- Data dictionary
- A document describing each variable, unit and meaning.
- Principal component analysis
- A technique that summarises variation using orthogonal combinations of variables.
- Outlier
- An observation far from the rest, which may be an error or a real event.
- Reproducible workflow
- An analysis that can be rerun from raw data to results.
Working together on AI, Machine Learning & Data Science projects
AI and data science engagements start with the decision the model is meant to support and with an audit of the data: sensors, sampling, labels, missing values, the structure of the data by specimen, machine or time, and how future data will differ from past data. We ask for a representative sample and a description of the process. Validation is designed around that structure to avoid leakage, and results are compared with simple baselines. Reports give performance with uncertainty, failure cases and the conditions under which the model should not be trusted. No performance figures are promised in advance.
Frequently asked questions
Do you handle confidential industrial data?
Confidentiality arrangements are agreed before data are shared. We recommend sharing only the minimum needed and avoiding sensitive uploads through the public contact form.
What data formats can you handle?
Common tabular, time-series, image and log formats can be handled. Unusual proprietary formats may need conversion, which we can discuss.
Can you work with messy test-campaign data?
Yes. Cleaning and documentation are part of the work, and the issues found are reported.
Do you provide the code?
Yes, scripted workflows can be provided so that analyses are reproducible.
Is confidential data a problem?
Confidentiality terms can be agreed in writing; avoid sending sensitive data through the public form.
How is this service scoped and priced?
After your enquiry we review what you need, ask for missing information and send a written scope and quotation. Price depends on complexity, deliverables and timeline. See the project workflow.