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.
Maintenance strategies have evolved from fixing machines when they break, through scheduled replacement, to condition-based and predictive approaches that act on evidence of actual state. The promise is fewer unplanned stoppages and less unnecessary maintenance. The difficulty is that failures are rare, data about them are scarce and the signals that precede failure are often subtle, noisy and specific to the machine and its operating conditions.
Predictive maintenance analytics therefore begins with understanding failure modes and available measurements, not with algorithms. Which faults matter, which signals carry information about them, how early can they be detected, and what lead time does the maintenance organisation need to act? The answers determine whether anomaly detection, degradation modelling or remaining-useful-life estimation is appropriate and how success should be measured.
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
- Few recorded failures to learn from
- Sensors that capture symptoms but not causes
- Alarms that fire too often to be actionable
Methods and analytical approaches
- Health-indicator construction from sensor data
- Anomaly detection and degradation modelling
- Remaining-useful-life approaches (data-driven and physics-informed)
- Evaluation with realistic lead-time metrics
Applications
- Rotating machinery monitoring
- Fleet-level maintenance analytics
- Battery and component ageing studies
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: sensor and maintenance-log data
Typical inputs required from you
- Sensor and maintenance-log data
- Failure definitions and timestamps
- Operating-condition information
Deliverables
- Health-indicator and model description
- Evaluation of detection lead time and false alarms
- Data-collection recommendations
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
Failure modes, data and maintenance context
We start from failure modes and effects: what fails, how, with what consequences and what physical changes accompany degradation. Available data, such as vibration, temperature, current, pressure, oil analysis and operating conditions, are mapped to those failure modes. Maintenance records are essential and often unreliable: dates, causes and replaced components may be missing or inconsistent. Their quality limits supervised modelling, and we assess it early. The operational context decides which prediction horizon and false-alarm rate are tolerable.
Health indicators from sensor data
A health indicator is a quantity derived from sensors that tracks the condition of a component, for example band-limited vibration energy, a bearing defect-frequency amplitude or a residual between measured and expected temperature. Good indicators are monotonic or trendable as degradation advances, sensitive to the fault and robust to operating changes. Normalisation for load and speed is crucial, since changes in operating conditions can mimic or hide degradation.
Anomaly detection and degradation modelling
Where failure examples are few, models of normal behaviour can flag departures from it. Statistical limits on residuals, distance measures or learned models of normal data identify anomalies, and thresholds are set according to the cost of false alarms and missed detections. Where degradation progresses over time, trend models, state-space or stochastic process models describe how health evolves and how uncertainty grows. We distinguish detection, diagnosis and prognosis, because each needs different data.
Remaining useful life estimation
Remaining-useful-life estimates the time until a component reaches a failure threshold. Data-driven approaches learn from run-to-failure histories, which are rare; physics-informed approaches combine degradation physics with data; hybrid approaches use both. Estimates should be provided with uncertainty intervals that narrow as failure approaches. Where run-to-failure data are insufficient, we are explicit that estimates are tentative and recommend what data would improve them.
Evaluation with operationally meaningful metrics
Standard classification accuracy is a poor measure of maintenance value. We evaluate lead time before failure, false alarm rate per operating time, detection rate for genuine faults and the effect on maintenance cost under stated assumptions. Validation is done by machine or fleet group, not random sampling of time points, so that the model is tested on machines it has not seen. Results are reported with confidence intervals and caveats about the small number of failures.
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
- Starting with an algorithm. Without a failure-mode analysis, models may detect operating changes rather than faults.
- Trusting unreliable maintenance records. Incorrect failure dates or causes mislead supervised models and evaluation.
- Ignoring operating conditions. Load and speed changes can produce signal changes larger than those from degradation.
- Evaluating on random time splits. Neighbouring time points are nearly identical, leaking information between training and test.
What good reporting looks like
A predictive maintenance report describes the machine and failure modes, data sources and quality, health indicators and their physical justification, the detection or prognostic models, the validation design by machine or fleet, performance in terms of lead time and false alarm rate with uncertainty, the limitations including the number of failures available and the conditions covered, and recommended deployment and monitoring steps. It does not promise the avoidance of failures or specific cost savings.
An illustrative scenario
Hypothetical example written for explanation. It is not a client case, a result or a testimonial.
Imagine a researcher with vibration records from twelve identical pumps, of which three failed during the observation period. A health indicator based on bearing-related spectral energy, normalised for speed and flow, increases steadily in the weeks before each failure. A simple model of normal behaviour detects the departure several days ahead in two of the three cases and misses the third, where the failure was due to a different cause. The researcher reports the lead times, the single miss and the false-alarm rate on the healthy pumps, and notes that three failures are too few for strong conclusions.
Questions to settle before you begin
- Which failure modes cause the greatest loss?
- Which signals plausibly carry early information about them?
- How reliable are my maintenance records?
- What lead time and false-alarm rate can the organisation tolerate?
- How many machines and failures can I validate on?
Key terms
- Health indicator
- A quantity derived from data that tracks the condition of a component.
- Remaining useful life
- The estimated time until a component reaches a failure criterion.
- Prognostics
- Prediction of future degradation and failure.
- Failure mode
- The way in which a component fails.
- Lead time
- The time between a warning and the failure or required action.
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
What if I have no failure data?
Anomaly detection and physics-based approaches may still be feasible, but remaining-life claims would not be supportable. We say so up front.
Can you predict failures with no failure data?
Anomaly detection against normal behaviour is possible. Predicting time to failure without failure examples is much less reliable and will be described as such.
What sensors do I need?
That depends on the failure modes. A review can identify which sensing would be informative and whether current sensors suffice.
Do you implement real-time systems?
The service focuses on analytics and validation. Real-time implementation can be discussed within an agreed scope.
How long until the model is useful?
It depends on data availability and failure frequency. Feasibility is assessed at scoping, without promising results.
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.