I need a predictive maintenance model that explains its reasoning and does not operate as a black box.

An explainable predictive maintenance (PdM) model relies on interpretable machine learning algorithms or Explainable AI (XAI) frameworks to provide clear, human-readable reasoning for every predicted equipment failure.

Instead of acting as an opaque black box, an open-box predictive maintenance framework exposes the underlying physical or statistical triggers—such as specific vibration frequencies, temperature spikes, or wear thresholds—enabling maintenance engineers to verify the root cause before taking action.

Core Architecture of an Explainable PdM Model

• Interpretable Base Algorithms: Uses intrinsically transparent models like Decision Trees, Generalized Additive Models (GAMs), or Survival Analysis rather than deep neural networks.
• Post-Hoc Explainability Tools: Applies additive feature attribution methods like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to complex models to quantify exactly how much each sensor reading contributed to a failure warning.
• Physics-Informed Integration: Combines machine learning with explicit degradation formulas (e.g., Paris' law for crack growth or Arrhenius models for thermal aging) so predictions align with known physical laws.
• Root Cause Diagnostics: Outputs a ranked list of contributing anomaly factors alongside the remaining useful life (RUL) estimation.

Comparison of Explainable vs. Black-Box Approaches

Recommended Implementation Steps

If you'd like, let me know:

• What type of equipment you are monitoring (e.g., pumps, turbines, motors)
• What sensors and data you currently have available (vibration, acoustics, temperature)

I can help you choose the best modeling approach for your specific setup.

Yes. If non-black-box reasoning is a hard requirement, I would avoid making a deep neural network the primary decision model. A strong design is an interpretable predictive-maintenance pipeline built around survival analysis + monotonic/gradient-boosted models, with every prediction accompanied by evidence.

Recommended architecture

Sensors → engineered degradation features → interpretable risk/RUL model → explanation → maintenance recommendation

1. Predict two things separately

• Failure probability: probability of failure within 7, 30, 60, etc. operating cycles/hours.
• Remaining useful life (RUL): estimated time/cycles until failure, preferably as a range rather than a single number.

A Cox proportional-hazards model is particularly attractive for the failure-risk component because its coefficients directly describe how variables affect hazard. Recent predictive-maintenance work has used Cox models specifically to provide interpretable feature selection and failure-risk reasoning.

2. Use physically meaningful features

Instead of feeding hundreds of raw sensor readings into an opaque model, construct features such as:

• temperature trend over the last 10 cycles
• vibration increase over the last 20 cycles
• pressure deviation from the asset's healthy baseline
• rate of change
• rolling mean/std
• deviation from expected operating conditions
• cumulative operating hours/cycles
• time since last maintenance

This makes the model's inputs understandable to maintenance engineers.

3. Use an interpretable ML model for nonlinear effects

For RUL, I would use gradient-boosted decision trees such as XGBoost, with carefully controlled features and constraints.

You can then expose exactly why a prediction changed:

Predicted RUL: 38 cycles Baseline RUL: 71 cycles

Main contributors

• Bearing temperature trend: −17 cycles
• Vibration increase: −11 cycles
• Pressure efficiency loss: −8 cycles
• Operating age: −5 cycles
• Stable oil temperature: +3 cycles Gradient boosting is already being investigated as a transparent alternative to deep learning for RUL prediction, particularly where interpretability is important.

The important part: make the reasoning intrinsic

I wouldn't rely solely on SHAP and call the model "explainable." SHAP is useful, but it's still a post-hoc explanation of a potentially complicated model.

Instead, build explanations into the model:

Asset 1842
────────────────────────────────
Failure risk:       23% in 30 cycles
Estimated RUL:      42 cycles
Confidence range:   31–58 cycles

WHY?
  ↑ Vibration trend       HIGH impact
  ↑ Bearing temperature   MEDIUM impact
  ↓ Pressure efficiency   MEDIUM impact
  → Operating load        LOW impact

TREND
  Vibration       ▁▂▃▄▅▆▇
  Temperature     ▁▂▂▃▄▅▆

RECOMMENDATION
  Inspect bearing system within 20 cycles.

REASON
  Vibration has increased 34% over 15 cycles
  and the temperature trend is simultaneously rising.

That is much more useful operationally than:

"Neural network predicts failure with 87% confidence."

Add a counterfactual explanation

The system should also answer:

"What would have to change for the risk to become acceptable?"

For example:

Current 30-cycle failure probability: 23% If vibration trend falls below 0.018 g/cycle, estimated probability falls to 9%. This gives engineers something actionable rather than merely an alarm.

Add uncertainty

Don't output:

RUL = 42 cycles. Output:

RUL = 42 cycles (90% interval: 31–58) And distinguish between:

• model uncertainty
• sensor/data quality
• asset conditions unlike training data
• extrapolation beyond observed degradation

If the system has insufficient evidence, it should explicitly say "insufficient evidence" rather than manufacture a confident prediction.

A good production stack

I'd build it roughly as:

                 ┌─────────────────────┐
Sensor telemetry │ temperature         │
────────────────►│ vibration           │
                 │ pressure            │
                 │ current/load        │
                 └──────────┬──────────┘
                            ↓
                 ┌─────────────────────┐
                 │ Feature engineering │
                 │ trends / deviations │
                 │ degradation rates   │
                 └──────────┬──────────┘
                            ↓
              ┌─────────────┴─────────────┐
              ↓                           ↓
       Cox survival model         Gradient boosting
       Failure probability              RUL
              ↓                           ↓
              └─────────────┬─────────────┘
                            ↓
                  ┌──────────────────┐
                  │ Explanation layer│
                  │                  │
                  │ Why?             │
                  │ What changed?    │
                  │ What matters?    │
                  │ What if?         │
                  └────────┬─────────┘
                           ↓
                  Maintenance action

This also gives you a clean audit trail: sensor values → features → model factors → risk/RUL → recommendation.

For a prototype, NASA's C-MAPSS turbofan dataset is a good benchmark because it contains multivariate sensor trajectories and RUL targets. Kaggle Hugging Face There are also recent interpretable PdM frameworks combining survival analysis, tree-based models, temporal features, and explanation layers.

If you're building this for a real industrial asset, I can also lay out the actual Python architecture, database schema, model training pipeline, explainability logic, and API/dashboard design for this system.

• model uncertainty
• sensor/data quality
• asset conditions unlike training data
• extrapolation beyond observed degradation

If the system has insufficient evidence, it should explicitly say "insufficient evidence" rather than manufacture a confident prediction.

A good production stack

I'd build it roughly as:

                 ┌─────────────────────┐
Sensor telemetry │ temperature         │
────────────────►│ vibration           │
                 │ pressure            │
                 │ current/load        │
                 └──────────┬──────────┘
                            ↓
                 ┌─────────────────────┐
                 │ Feature engineering │
                 │ trends / deviations │
                 │ degradation rates   │
                 └──────────┬──────────┘
                            ↓
              ┌─────────────┴─────────────┐
              ↓                           ↓
       Cox survival model         Gradient boosting
       Failure probability              RUL
              ↓                           ↓
              └─────────────┬─────────────┘
                            ↓
                  ┌──────────────────┐
                  │ Explanation layer│
                  │                  │
                  │ Why?             │
                  │ What changed?    │
                  │ What matters?    │
                  │ What if?         │
                  └────────┬─────────┘
                           ↓
                  Maintenance action

This also gives you a clean audit trail: sensor values → features → model factors → risk/RUL → recommendation.

To build a predictive maintenance model that explains its reasoning and avoids the black-box trap, you need a white-box (interpretable) approach . Instead of complex deep learning networks, these methods rely on transparent logic, explicit rules, or feature contributions you can audit.

Here are the top approaches for building an interpretable predictive maintenance model:

• Decision Trees and Rule Sets: Algorithms like CART or Decision Trees split data based on clear threshold conditions (e.g., If vibration > 4.5 mm/s AND temperature > 75°C, then failure predicted within 48 hours). They map out an exact flowchart of logic.
• Generalized Additive Models (GAMs): Models like Explainable Boosting Machines (EBMs)Explainable Boosting Machines (EBMs)E x p l a i n a b l e B o o s t i n g M a c h i n e s(E B M s) use glass-box machine learning. They blend the accuracy of gradient boosting with a restriction that features are modeled with shape functions, letting you see the exact mathematical contribution of every sensor reading (like pressure or RPM) to the final risk score.
• Survival Analysis: Traditional statistical methods like the Cox Proportional Hazards Model calculate a baseline hazard function and multiply it by exponential risk factors. It outputs a clear hazard ratio showing how specific covariates (e.g., operating hours, load cycles) increase the risk of equipment failure over time.
• Post-Hoc Interpretability Tools (SHAP/LIME): If you must use a high-performance ensemble model (like Random Forest or XGBoost) for complex sensor interactions, wrap it with SHAP (SHapley Additive exPlanations)SHAP (SHapley Additive exPlanations)S H A P(S H a p l e y A d d i t i v e e x P l a n a t i o n s) or LIME. SHAP values mathematically assign each feature (e.g., oil pressure, acoustic emission) a specific positive or negative impact value for that exact prediction.

To help us narrow down the best architecture, could you share:

• What kind of equipment or machinery are you monitoring?
• What type of data do you have available (e.g., high-frequency vibration streams, daily logs, or event-based maintenance history)?

For a predictive maintenance system that explains its reasoning, avoid treating explainability as an afterthought. Design the model so that maintenance engineers can answer:

• Why does the model think this machine will fail?
• Which sensor readings contributed to the prediction?
• What evidence suggests a component is degrading?
• What action should be taken and why now?

A good architecture is an explainable predictive maintenance (XPM) pipeline. Explainable AI methods such as SHAP and LIME are commonly used to expose feature contributions, while inherently interpretable models such as decision trees and rule systems can provide direct reasoning paths.

Recommended model design

1. Collect machine condition data

Inputs might include:

• Vibration measurements - RMS vibration
• frequency peaks
• bearing fault indicators
• Temperature - average temperature
• temperature spikes
• thermal drift
• Pressure, flow, voltage, current
• Operating hours
• Load conditions
• Maintenance history
• Component replacement history

Instead of feeding raw signals directly into a deep model, create features with engineering meaning:

Example:

Bearing health score =
    vibration trend +
    vibration frequency anomaly +
    operating hours +
    lubrication interval

This makes explanations easier to understand.

---

2. Use an interpretable model first

A strong starting point is a model that is naturally explainable:

Option A: Explainable boosting model (EBM)

Example output:

Failure probability: 82% Reasoning:

• Vibration increase: +35%
• Temperature instability: +22%
• 4,500 hours since bearing replacement: +18%
• High operating load: +7%

The model can show how each factor changes the prediction.

Option B: Decision tree / rule-based model

Example:

IF vibration > 8 mm/s
AND temperature trend > 15% increase
AND bearing age > 10,000 hours

THEN
    High risk of bearing failure

Maintenance teams often prefer this because it resembles existing troubleshooting procedures.

---

3. Add machine learning for accuracy with explanation

If more predictive power is needed, use models such as:

• Gradient boosted trees
• Random forests
• XGBoost

Then attach an explanation layer.

A common approach is:

Sensors
   ↓
Feature engineering
   ↓
Prediction model
   ↓
Explanation engine
   ↓
Maintenance recommendation

SHAP (SHapley Additive Explanations) is widely used because it can provide both:

• Global explanations — what generally drives failures
• Local explanations — why this specific machine received this prediction

ScienceDirect DOI

4. Produce human-readable explanations

Instead of:

"Failure probability = 0.87" Return:

Predicted issue: Motor bearing degradation

Confidence: 87%

Main reasons:

• Vibration increased 28% over the last 14 days
• Bearing temperature is 12°C above its normal operating range
• Lubrication interval exceeded by 320 hours

Recommended action: Inspect bearing lubrication and schedule vibration analysis within 7 days.

---

5. Include uncertainty

A trustworthy maintenance model should say when it is unsure.

Example:

Prediction:
Bearing failure risk: 72%

Confidence:
Moderate

Reason:
Sensor pattern resembles previous failures,
but similar cases have limited historical data.

Avoid forcing a confident prediction from weak evidence.

---

6. Add domain knowledge constraints

The best systems combine:

• Machine learning patterns
• Engineering rules
• Physics knowledge

Example:

A model should not claim:

"Low temperature caused bearing failure" if engineers know low temperature is actually normal during startup.

Physics-informed approaches can improve trust by ensuring explanations align with equipment behavior.

---

Example technology stack

---

A practical first version

For many industrial environments, I would build:

1. XGBoost classifier

• Predict failure within next 7/30 days

2. SHAP explanation layer

• Rank contributing factors

3. Maintenance rule engine

• Convert model output into actions

4. Engineer feedback loop

• Allow technicians to confirm or reject predictions

This gives high predictive performance while keeping the reasoning visible. Explainable predictive maintenance research increasingly uses this combination of predictive models plus SHAP/LIME-style explanation layers to make AI recommendations auditable by maintenance teams.

If you are building this for a specific asset type (motors, pumps, turbines, vehicles, CNC machines, etc.), the model architecture and features should be adjusted accordingly.

---

Example technology stack

Example:

Prediction:
Bearing failure risk: 72%

Confidence:
Moderate

Reason:
Sensor pattern resembles previous failures,
but similar cases have limited historical data.

Avoid forcing a confident prediction from weak evidence.

---

6. Add domain knowledge constraints

The best systems combine:

• Machine learning patterns
• Engineering rules
• Physics knowledge

Example:

A model should not claim:

"Low temperature caused bearing failure" if engineers know low temperature is actually normal during startup.

Physics-informed approaches can improve trust by ensuring explanations align with equipment behavior.

---

Example technology stack

Yes. If your priority is “the model must explain why it predicts a failure”, I would avoid making a deep neural network the primary decision-maker. A strong design is an interpretable predictive-maintenance pipeline with a transparent model at its core and explanations at the individual prediction level.

Recommended architecture

1. Predict equipment health/failure

• Use a GAM (Generalized Additive Model), Cox proportional-hazards model, or carefully constrained gradient-boosted trees.
• For Remaining Useful Life (RUL), a survival-analysis approach is particularly attractive because it naturally models time-to-failure and can handle censored observations.
• Recent work has demonstrated Cox-guided predictive maintenance and GAM-based RUL modeling specifically to improve interpretability.

2. Engineer physically meaningful features Instead of feeding thousands of raw sensor values into a black box, calculate features such as:

• vibration RMS / kurtosis
• bearing temperature
• motor current
• pressure
• operating hours
• temperature trend
• vibration trend
• number of recent overload events

This makes explanations meaningful to maintenance engineers. A 2026 industrial study, for example, identified seven physically interpretable process variables—including pressure, temperature, torque and cutting force—as the important predictors of equipment risk.

3. Produce an explicit explanation with every prediction

For example:

Failure probability in next 7 days: 78%
Risk increased because:
• Bearing vibration RMS increased 34% → +21 percentage points
• Bearing temperature increased 11°C → +13 pp
• Motor current variability increased → +8 pp
Risk reduced because:
• Operating load is currently below normal → −5 pp

Recommended action: inspect bearing and lubrication system within 24 hours.

That's substantially more useful than “neural network says failure = 0.78.”

If you need higher predictive accuracy

A good compromise is:

Gradient boosting (XGBoost/LightGBM) → SHAP → maintenance recommendation

This isn't intrinsically interpretable—the underlying model remains complex—but SHAP can quantify each feature's contribution to each prediction. Recent predictive-maintenance research has successfully combined gradient boosting with SHAP for RUL/failure prediction, while another 2026 study used ensemble models plus SHAP to expose the sensor variables driving predictions.

I would distinguish this from a genuinely transparent model:

My preferred design

For an industrial deployment, I'd build a two-layer model:

Layer 1 — transparent risk model

Sensors → engineered physical features → GAM/Cox model → failure probability + RUL

Layer 2 — explanation & decision support

Prediction → feature contributions → trend explanation → recommended maintenance action

The explanation should include global reasoning (“what generally causes failures?”) and local reasoning (“why does this particular machine have elevated risk?”). That's important because XAI research specifically identifies different explanation needs for predictive-maintenance users.

I'd also add counterfactual explanations, e.g.:

“If vibration RMS were 18% lower and bearing temperature returned below 72°C, predicted 7-day failure risk would fall from 78% to approximately 31%.”

This is an emerging direction for predictive maintenance because it turns an explanation into a potential maintenance intervention rather than merely showing feature importance. A 2026 study proposes counterfactual explanations specifically for survival/RUL models on multivariate maintenance time series.

Bottom line

If non-black-box reasoning is a hard requirement, my first choice would be:

GAM/Cox survival model + physically meaningful sensor features + uncertainty estimates + counterfactual explanations.

If predictive performance proves insufficient, move to:

XGBoost/LightGBM + SHAP + constrained features + counterfactual explanations.

That gives you a system where maintenance personnel can see what the model believes, why it believes it, how confident it is, and what condition would change its recommendation, rather than simply receiving a failure score. Recent literature supports this explainability-first approach for industrial predictive maintenance.

To build a predictive maintenance model that avoids the "black box" trap and clearly explains its reasoning, you should leverage explainable machine learning or interpretable white-box models.

Here is a structured approach and the best modeling frameworks to achieve transparent predictive maintenance:

• Decision Trees / Classification and Regression Trees (CART) : These provide a literal flowchart of decisions (e.g., If vibration > 4.5 mm/s and temperature > 75°C, then failure predicted within 48 hours ). They are natively transparent and easy for engineers to audit.
• Extreme Gradient Boosting (XGBoost) or Random Forests with SHAP (SHapley Additive exPlanations) : Tree ensembles offer high predictive accuracy. By pairing them with post-hoc explanation frameworks like SHAP or LIME (Local Interpretable Model-agnostic Explanations), you can output the exact feature contribution (e.g., high oil pressure pushed the risk score up by +30%) for every individual prediction.
• Generalized Additive Models (GAMs) / Explainable Boosting Machines (EBMs) : EBMs are glass-box models that capture non-linear relationships just like advanced boosting trees, but remain completely transparent by design. You can visually inspect the exact shape of how a feature (like operating hours or RPM) impacts the failure risk.
• Survival Analysis (e.g., Cox Proportional Hazards Model) : If your goal is predicting Time-to-Failure (TTF), survival models explicitly show how specific covariates increase or decrease the hazard rate over time without complex neural network layers.
• Physics-Informed Neural Networks (PINNs) or Hybrid Models : If using deep learning, embed known physical laws (such as thermal degradation equations or wear-and-tear formulas) into the loss function or architecture. This ensures the network's reasoning aligns with actual mechanical engineering principles rather than arbitrary data correlations.

To help narrow down the ideal architecture, could you share:

• What type of sensor data or failure logs do you have (vibration, temperature, acoustic, logs, run-hours)?
• Are you predicting a binary failure (will/won't fail in X days) or remaining useful life (RUL)?

To avoid a black-box approach in predictive maintenance, you should implement an Interpretable White-Box Model or an Explicitly Guided Explainable AI (XAI) framework . Instead of using opaque deep neural networks that output a naked failure score, transparent architectures rely on explicit physical rules, symbolic logic, or inherently clear generalized additive parameters. When an alert triggers, the system explicitly identifies which condition—such as bearing vibration frequency anomalies, thermal runaway spikes , or torque variations —violated a specific operational threshold or logic rule.

Transparent Predictive Maintenance Architectures

• Decision Trees and Rule-Based Classifiers : Use hierarchical splits based on physical sensor bounds. They trace an exact path (e.g., if vibration > 4.5 mm/s AND temperature > 75°C, then fault code B-3 is active).
• Generalized Additive Models (GAMs) : Blend the flexibility of non-linear relationships with strict feature additivity, allowing engineers to visualize the exact mathematical contribution of each sensor stream.
• Neuro-Symbolic AI / Logic Tensor Networks : Combine neural anomaly detection with symbolic logic rules. The system yields a prediction wrapped in symbolic reasoning, validating which domain constraints or failure modes were satisfied.
• Post-Hoc Local Explanations (SHAP/LIME) : If complex data structures require advanced algorithms, layer on SHAP (Shapley Additive exPlanations) to attribute exact credit values to each sensor metric for every individual prediction.

Comparison of Transparent Modeling Strategies

If you'd like to narrow down the design, tell me:

• What type of equipment you are monitoring (e.g., rotating machinery, pumps, electrical transformers)?
• What sensor data streams are currently available (vibration, acoustics, temperature, motor current)?
• Do you prefer a fully glass-box model (like decision rules) or a post-hoc explanation layer over a more complex model?

If you need a predictive maintenance model that explains its reasoning, avoid starting with a deep neural network or other opaque model. A good design is an interpretable predictive maintenance system where every prediction can be traced to measurable equipment conditions. Explainable AI (XAI) approaches are increasingly used in predictive maintenance specifically to address trust and transparency problems with black-box models.

A practical architecture would look like this:

1. Define the prediction target

Choose one of these:

• Failure probability
  • Example output: “Bearing failure risk in next 7 days: 82%”
• Remaining Useful Life (RUL)
  • Example output: “Estimated remaining operating time: 240 hours”
• Maintenance recommendation
  • Example output: “Inspect lubrication system within 48 hours”

2. Use interpretable input features

Instead of feeding raw sensor streams directly into a black box, engineer features that maintenance engineers understand:

The model should be able to say:

“Failure risk increased because vibration rose 35%, bearing temperature exceeded its normal range, and the machine has operated 1,200 hours since the last bearing inspection.”

3. Choose an explainable model

Good candidates:

Option A — Decision tree / rule-based model

Most transparent.

Example:

IF vibration_RMS > 8 mm/s
AND bearing_temperature > 85°C
AND operating_hours > 1000
THEN
  Failure risk = HIGH
  Recommended action = Inspect bearing

Advantages:

• Humans can audit every rule
• Easy approval in regulated environments
• Easy troubleshooting

Tradeoff:

• May be less accurate for complex patterns

---

Option B — Explainable boosting model (EBM)

An Explainable Boosting Machine learns nonlinear relationships while still showing feature-by-feature reasoning.

Example explanation:

Prediction: 76% failure probability

Contributing factors:
+32% vibration increase
+21% temperature instability
+15% lubrication pressure drop
-8% recent maintenance completed

This often provides a strong balance between accuracy and interpretability.

---

Option C — Gradient boosting + SHAP explanations

A model such as XGBoost or LightGBM can be paired with SHAP values to explain individual predictions. SHAP is commonly used in explainable predictive maintenance research because it shows how each feature contributes to a specific prediction.

Example:

Asset: Pump #42

Predicted failure risk: 91%

Why:
  ↑ Bearing vibration:
      +38% risk contribution

  ↑ Motor temperature:
      +27% risk contribution

  ↓ Flow rate:
      +18% risk contribution

  Recent lubrication service:
      -9% risk reduction

4. Add a maintenance reasoning layer

Do not stop at “failure likely.”

Translate predictions into actions:

IF:
  vibration increasing > 20% over 14 days
AND:
  temperature trend increasing
AND:
  lubrication interval exceeded

THEN:
  Recommend:
  "Schedule bearing inspection during next planned shutdown."

This combines machine learning with engineering knowledge.

5. Include confidence and uncertainty

Every prediction should include:

• Confidence score
• Data quality status
• Similar historical failures
• Explanation of limitations

Example:

Risk: HIGH (84%)

Confidence:
High — based on 37 similar historical degradation patterns

Main evidence:
1. Increasing vibration
2. Rising bearing temperature
3. Reduced efficiency

Recommended verification:
Perform vibration spectrum analysis before replacement.

6. Suggested production design

Sensors
   |
   v
Feature Engineering
   |
   v
Interpretable Model
(EBM / Decision Tree / Explainable Boosting)
   |
   +----------------+
   |                |
Prediction       Explanation
   |                |
Failure risk    Why it happened
RUL estimate    Which factors matter
   |
Maintenance recommendation

For most industrial deployments, I would start with:

Explainable Boosting Machine + engineering rules + SHAP validation

because it gives:

• understandable reasoning,
• strong predictive performance,
• auditability,
• easier adoption by maintenance teams.

Research on explainable predictive maintenance also emphasizes that explanations are not just for end users—they help engineers debug models, validate predictions, and build trust in automated maintenance decisions.

If you want, I can also provide a reference implementation (Python architecture) or a full system design for a factory/industrial asset scenario.

Yes. If “not a black box” is a hard requirement, I would design the predictive-maintenance system around an intrinsically interpretable model, rather than putting SHAP/LIME on top of a neural network and calling it explainable.

Recommended architecture

Sensors → interpretable features → interpretable risk/RUL model → maintenance recommendation → human-readable reason

For example, suppose you're monitoring a motor with:

• vibration
• temperature
• current
• pressure
• RPM
• operating hours
• historical maintenance/failure data

I would build two complementary predictions:

1. Failure risk: probability of failure within the next 7/30/90 days.
2. Remaining useful life (RUL): estimated operating hours/cycles remaining.

Model choice

My first choice would be an Explainable Boosting Machine (EBM) / generalized additive model, or a carefully constrained gradient-boosting model if more predictive power is necessary.

An additive model can essentially say:

Failure risk = baseline risk + vibration contribution + temperature contribution + current contribution + age contribution + operating-condition contribution

That makes the model's reasoning inspectable rather than hidden inside thousands of neural-network parameters.

For example:

The resulting explanation could be:

High failure risk (78%). The dominant contributors are elevated vibration (+31%), high bearing temperature (+18%), and accumulated operating hours (+11%). Vibration has increased 24% over the last 14 days, which is consistent with developing bearing degradation. Recommended action: inspect the bearing and lubrication system within 48 hours.

That's substantially more useful to a maintenance technician than “the neural network predicts failure with 78% confidence.”

Don't stop at feature importance

I'd require four kinds of explanation:

1. Global reasoning
Show how each sensor affects risk across its entire operating range.

For example:

Vibration < 3 mm/s → little additional risk
3–5 mm/s → moderate risk
5–7 mm/s → elevated risk

7 mm/s → rapidly increasing risk

This lets an engineer inspect the actual learned relationship.

2. Local reasoning
For a particular machine, show exactly why its current prediction is high or low.

3. Temporal reasoning
Don't merely say that temperature is important. Show:

Temperature: 72°C → 76°C → 81°C → 87°C over 10 days

and connect the trend to the prediction.

4. Counterfactual reasoning
Tell the operator what would need to change:

“If vibration falls below 5.0 mm/s while temperature remains below 80°C, predicted 30-day failure risk falls from 78% to approximately 35%.”

That is especially valuable for maintenance decisions.

Where SHAP fits

SHAP can be useful, but I would treat it as a secondary validation/explanation layer, not as the fundamental justification for using an opaque model. SHAP and LIME are widely used in predictive-maintenance research, including RUL prediction and rotating-machine fault diagnosis.

There is an important distinction:

Black-box model + SHAP

“I don't know how the model works, but here's an approximation of why it made this prediction.”

versus

Interpretable model + transparent feature effects

“Here is the actual mathematical structure of the model and the contribution of every relevant variable.”

For your requirement, I'd favor the second.

I'd also build in safety/engineering constraints

The model shouldn't be allowed to learn physically nonsensical relationships.

For example, if engineering knowledge says that increasing bearing temperature should never reduce failure risk, impose a monotonicity constraint.

Likewise:

• enforce physically meaningful feature ranges
• distinguish operating regimes
• detect sensor failures
• expose missing/stale sensor data
• quantify prediction uncertainty
• provide a “model doesn't know” state
• log every prediction and explanation
• allow engineers to audit the model's learned relationships

This is particularly important because research on explainable predictive maintenance points out that simply adding an XAI technique does not automatically make explanations appropriate or reliable for maintenance users.

A practical system

I would structure the production system roughly like this:

                 ┌─────────────────────┐
Sensors ────────►│ Data quality checks  │
                 └──────────┬──────────┘
                            │
                            ▼
                 ┌─────────────────────┐
                 │ Feature engineering │
                 │ • rolling averages  │
                 │ • trends             │
                 │ • vibration features │
                 │ • operating regime   │
                 └──────────┬──────────┘
                            │
                 ┌──────────▼──────────┐
                 │ Interpretable model │
                 │                     │
                 │ Failure probability │
                 │ RUL estimate        │
                 └──────────┬──────────┘
                            │
              ┌─────────────┼─────────────┐
              ▼             ▼             ▼
        Risk score      Explanation   Uncertainty
              │             │             │
              └─────────────┼─────────────┘
                            ▼
                 ┌─────────────────────┐
                 │ Maintenance action  │
                 │ recommendation      │
                 └─────────────────────┘

There is also current research specifically combining explainable predictive models with maintenance scheduling, using sensor-derived RUL/failure predictions and explanations before optimizing the actual maintenance schedule.

If you give me your equipment type and the data you have (for example, CSV columns/sensor names, sampling rate, and what constitutes a failure), I can design the actual model—including the features, target variable, interpretable algorithm, explanation format, and validation strategy.

To build a predictive maintenance model that is fully transparent and explains its reasoning (avoiding the black-box nature of deep neural networks or complex gradient boosting trees), you need an interpretable machine learning architecture.

Here is a structured framework and approach for designing a white-box or glass-box predictive maintenance model:

• Rule-Based Expert Systems / Decision Tables : Ideal if you have strict, well-documented engineering thresholds (e.g., vibration > $5 mm/s$ AND temperature > $80°C$ implies bearing failure). They offer 100% traceability but require heavy domain expertise.
• Explainable Boosting Machines (EBMs) : A form of Generalized Additive Models (GAMs) that automatically detects interactions between features while remaining completely transparent. You can visualize the exact shape function for every sensor reading.
• Survival Analysis (Cox Proportional Hazards) : Great for time-to-failure modeling. It outputs a clear hazard ratio showing how specific variables (like pressure or operating hours) multiplicatively increase or decrease the risk of failure over time.
• Shallow Decision Trees : Limited to a small maximum depth (e.g., depth of 3 to 5) so an operator can literally trace the path from root to leaf to see why a maintenance flag was raised.
• Post-Hoc Explainability (SHAP / LIME) : If you must use a high-performance black-box model (like Random Forest or XGBoost), wrap it with SHAP (SHapley Additive exPlanations) values. SHAP breaks down individual predictions into the exact positive or negative contribution of each sensor feature.

To help tailor the right architecture for you, tell me:

• What type of equipment or machinery are you monitoring?
• What sensor data or failure logs do you currently have available (e.g., vibration, temperature, run-hours)?
• Do you prefer a purely glass-box model (like a decision tree or EBM) or a black-box model with an explainability layer (like XGBoost + SHAP)?