Chapter 26: Explainable AI in Industrial Settings |
Summary |
Industrial artificial intelligence has matured to the point where predictive models can detect equipment failures, optimize production schedules, and reduce energy consumption with remarkable accuracy. Yet many manufacturers hesitate to deploy these systems on the factory floor. The reason is rarely a lack of confidence in what AI can predict. It is a lack of understanding of why the AI reaches its conclusions. This chapter examines the black box problem in industrial settings and shows how explainable AI, particularly the approach embodied by Via Co-Pilot, removes a critical barrier to adoption. Explainable diagnostics that identify root causes such as bearing wear or lubrication breakdown allow engineers to validate machine insights, build operator trust, and trigger automated work orders through collaborative workflows. We explore applications across automotive, aerospace, steel, chemicals, food processing, energy, pharmaceuticals, mining, pulp and paper, electronics, and heavy machinery. The chapter closes with a detailed summary of the lessons learned and the road ahead. |
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1. Introduction: Why Industry Cares About Explanation |
When a production line stops unexpectedly, the cost is measured in lost output, idle labor, missed delivery commitments, and sometimes damaged equipment. Maintenance teams have always relied on experience, vibration analysis, thermal imaging, and oil sampling to anticipate these failures. Modern AI systems can process thousands of sensor readings per second and flag anomalies long before a human notices a trend. The difficulty is that many of these systems cannot articulate the reasoning behind an alert. They produce a score, a probability, or a red light, and leave the engineer to guess what to do next. |
This is the black box problem. In a laboratory, a black box model that predicts failure with high accuracy may be celebrated. On a factory floor, the same model may be ignored. Operators and maintenance engineers are accountable for decisions that affect safety, cost, and uptime. They cannot act on a recommendation they do not understand, and they will not trust a system that cannot explain itself. The result is a gap between what AI can do and what industry is willing to adopt. |
Explainable AI closes that gap. Instead of delivering only a prediction, an explainable system delivers a diagnosis. It points to the likely root cause, shows the evidence that supports that conclusion, and presents the reasoning in language that engineers already use. A bearing that is wearing out produces specific vibration signatures. A lubrication breakdown changes temperature and friction patterns. An explainable system connects the sensor data to these physical mechanisms and says so plainly. The engineer can then validate the insight, compare it with their own experience, and decide whether to act. |
Via Co-Pilot is an example of this approach in practice. It was designed for industrial diagnostics, and its central design principle is that every alert must be accompanied by an explanation that a maintenance professional can evaluate. The system highlights root causes, such as bearing wear or lubrication breakdown, and enables collaborative workflows in which engineers can confirm or challenge the insight. Once confirmed, the insight can trigger automated work orders, closing the loop from detection to action. This transparency builds operator trust and accelerates adoption across plants and industries. |
The rest of this chapter is organized as follows. Section 2 defines the black box problem in industrial terms. Section 3 introduces explainable AI and its core ideas. Section 4 describes the Via Co-Pilot approach in detail. Sections 5 through 15 present real-world application examples from a wide range of industries. Section 16 discusses the human and organizational dimensions of trust. Section 17 considers limitations and open challenges. Section 18 offers a detailed summary and looks at future trajectories. |
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2. The Black Box Problem in Industrial Operations |
2.1 What Makes a Model a Black Box |
A black box model is one whose internal reasoning cannot be inspected or understood by a human. Deep neural networks with millions of parameters are the classic example. They map inputs to outputs through layers of mathematical transformations that are not human-readable. Even simpler models, such as gradient-boosted trees, can be difficult to interpret when they combine hundreds of features in complex ways. |
In consumer applications, this opacity is often acceptable. A music recommendation does not need to justify itself. In industrial operations, it is not acceptable. A maintenance engineer who receives an alert about a critical pump must decide whether to shut down a production line, dispatch a technician, or wait and observe. Each choice has consequences. Without an explanation, the engineer is being asked to act on faith. |
2.2 Why Opacity Blocks Adoption |
There are several reasons why opaque AI fails in industrial settings. |
First, accountability. When a decision affects safety or cost, someone must be responsible. If the AI cannot explain its reasoning, the human who acted on it bears the full burden. Many engineers are unwilling to accept that burden for a system they do not understand. |
Second, validation. Experienced engineers have deep knowledge of their equipment. They can often tell whether a recommendation makes sense if it is expressed in terms of physical causes. An unexplained alert gives them nothing to validate against. |
Third, troubleshooting. Even if an alert is correct, the engineer needs to know what to do. A prediction of imminent failure is less useful than a diagnosis of bearing wear, because the diagnosis points to a specific repair. |
Fourth, regulatory and safety requirements. In industries such as pharmaceuticals, aerospace, and nuclear power, decisions must be documented and justified. An opaque model makes compliance difficult. |
Fifth, organizational trust. Maintenance and operations teams develop trust over time through shared experience. A system that cannot explain itself cannot participate in that process. It remains an outsider, and outsiders are ignored. |
2.3 The Cost of Ignoring AI |
The irony is that ignoring AI also has costs. Unplanned downtime remains one of the largest sources of lost productivity in manufacturing. Studies across heavy industry consistently find that a significant share of equipment failures are preceded by detectable warning signs. When those signs are missed, the result is emergency repairs, expedited shipping, overtime labor, and sometimes secondary damage. Explainable AI is not a luxury. It is a practical tool for capturing value that opaque systems leave on the table. |
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3. Explainable AI: Core Ideas |
3.1 Transparency Versus Interpretability |
Two related terms appear in the literature. Transparency refers to the ability to see how a model works internally. Interpretability refers to the ability to understand the meaning of a model's output in human terms. In industrial settings, interpretability matters more than transparency. An engineer does not need to see every weight in a neural network. They need to understand why the system believes a bearing is failing. |
3.2 Post-Hoc Explanations |
One common approach is to build an accurate but opaque model and then add a separate explanation layer. Techniques such as feature importance, local surrogate models, and counterfactual explanations fall into this category. These methods can be useful, but they have a weakness. The explanation is an approximation of the model's behavior, not a description of its actual reasoning. In safety-critical settings, approximations can be misleading. |
3.3 Interpretable by Design |
A stronger approach is to build models that are interpretable by design. These models use features and structures that correspond to physical phenomena. For example, a diagnostic model might use vibration frequency bands that are known to be associated with specific bearing defects. The model's output is then naturally explainable because its inputs are meaningful. This is the approach that Via Co-Pilot follows. |
3.4 Explanation as a Workflow Component |
Explanation is not only a technical property. It is also a workflow component. An explanation that arrives as a standalone report has limited value. An explanation that appears alongside an alert, links to the relevant sensor data, invites engineer feedback, and can trigger a work order becomes part of the maintenance process. This is why collaborative workflows matter. Explanation and action must be connected. |
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4. The Via Co-Pilot Approach |
4.1 Design Principles |
Via Co-Pilot was built around four principles. |
First, every alert must have a cause. The system does not report anomalies without identifying a plausible physical root cause. |
Second, causes must be expressed in familiar terms. The system uses language that maintenance professionals already use, such as bearing wear, misalignment, lubrication breakdown, cavitation, and rotor imbalance. |
Third, engineers must be able to respond. The system provides a way for engineers to confirm, reject, or refine the diagnosis. Their feedback is recorded and used to improve future performance. |
Fourth, explanations must lead to action. Once a diagnosis is confirmed, the system can generate a work order, assign it to the right team, and attach the supporting evidence. |
4.2 Diagnostic Root Causes |
The system focuses on a set of root causes that are common across rotating equipment and industrial assets. These include bearing wear, lubrication breakdown, misalignment, imbalance, looseness, cavitation, corrosion, fouling, and electrical faults. For each cause, the system has a model of the sensor signatures that indicate it. When an anomaly is detected, the system evaluates these signatures and ranks the likely causes. |
4.3 The Explanation Interface |
The explanation interface presents three things. First, a plain-language statement of the diagnosis. Second, the evidence, including the sensor readings and trends that support it. Third, a recommended action, such as inspecting a specific bearing or checking lubricant quality. The interface is designed for use on the factory floor, not in a data science lab. It uses simple visuals and clear language. |
4.4 Collaborative Workflows |
The collaborative workflow is central to the design. When an alert appears, the engineer can review the evidence and mark the diagnosis as confirmed, uncertain, or incorrect. Confirmed diagnoses can trigger automated work orders. Uncertain diagnoses can be escalated to a specialist. Incorrect diagnoses are fed back into the system. Over time, this loop improves accuracy and builds a record of institutional knowledge. |
4.5 Automated Work Orders |
The connection to work orders is what turns insight into value. A diagnosis that sits in a dashboard does not reduce downtime. A diagnosis that creates a work order, assigns a technician, and reserves the needed parts does. The system integrates with common maintenance management platforms so that the handoff is seamless. |
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5. Automotive Manufacturing |
5.1 Robotic Arm Joints |
Automotive assembly lines rely on hundreds of robotic arms. Each joint contains bearings, gears, and servo motors. When a joint begins to fail, the line may slow down or stop. Via Co-Pilot monitors vibration and current signatures from the joint and identifies whether the issue is bearing wear, gear tooth damage, or lubrication breakdown. An explanation that points to lubrication breakdown allows the maintenance team to grease the joint during a scheduled break instead of replacing the entire assembly. |
5.2 Conveyor Systems |
Conveyors move parts through paint shops, body shops, and final assembly. Idler rollers and drive motors are common failure points. The system detects abnormal vibration patterns and explains whether the cause is a worn roller, a misaligned belt, or a failing motor bearing. Engineers can then prioritize repairs based on production impact. |
5.3 Stamping Presses |
Stamping presses operate under extreme loads. Hydraulic systems, clutches, and dies are all subject to wear. Explainable diagnostics help press operators distinguish between a hydraulic pressure issue and a mechanical misalignment. This distinction matters because the repair skills and downtime differ greatly. |
5.4 Paint Shop Fans |
Paint shops require precise air flow to avoid defects. Fans and blowers must operate reliably. The system monitors fan bearings and detects early signs of imbalance or wear. An explanation that identifies imbalance allows the team to clean or balance the fan before it damages the bearings. |
5.5 Weld Quality |
In body shops, welding robots must maintain consistent pressure and current. The system can correlate weld quality data with equipment condition and explain when a decline in quality is due to electrode wear rather than material variation. |
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6. Aerospace and Defense |
6.1 Engine Test Cells |
Aerospace engines are tested extensively before delivery. Test cells generate massive amounts of vibration, temperature, and pressure data. Explainable AI helps test engineers identify whether an anomaly is due to a test cell artifact or a genuine engine issue. The explanation must be precise, because the consequences of a missed defect are severe. |
6.2 Landing Gear Actuators |
Landing gear actuators are tested for thousands of cycles. The system monitors motor current and position feedback to detect bearing wear and lubrication breakdown. Engineers can then decide whether to overhaul the actuator or continue testing. |
6.3 Composite Layup Equipment |
Automated fiber placement machines lay carbon fiber tapes with high precision. Roller bearings and tensioners are critical. Explainable diagnostics help operators maintain the equipment and avoid defects in the composite structure. |
6.4 Ground Support Equipment |
Airlines and defense bases operate pumps, compressors, and generators. These assets are often older and have limited sensors. The system can work with whatever data is available and still provide a useful explanation, such as a likely bearing issue based on current signatures. |
6.5 Supply Chain Quality |
When a supplier part fails, explainable AI can help trace the failure to a manufacturing process. If a bearing fails due to inadequate lubrication, the explanation can prompt a review of the supplier's process. |
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7. Steel and Metals |
7.1 Rolling Mill Bearings |
Rolling mills operate under high load and high temperature. Bearing failures are costly and dangerous. The system monitors vibration and temperature and explains whether the cause is lubrication breakdown, contamination, or fatigue. This allows the mill to schedule a bearing change during a planned outage. |
7.2 Furnace Fans |
Furnace fans handle hot, dirty gas. Imbalance and erosion are common. Explainable diagnostics help maintenance teams decide when to clean or replace the fan rotor. An explanation that identifies erosion allows the team to plan for a replacement rather than a cleaning. |
7.3 Continuous Casters |
Continuous casters have many moving parts, including rollers and oscillators. The system detects anomalies and explains whether they are due to mechanical wear or process changes. This helps operators avoid unnecessary shutdowns. |
7.4 Hydraulic Systems |
Steel mills use large hydraulic systems for gates, shears, and presses. The system monitors pressure and flow and explains whether a problem is due to pump wear, valve leakage, or contamination. |
7.5 Overhead Cranes |
Cranes move heavy loads in harsh environments. Gearboxes and brakes are critical. Explainable AI helps inspectors focus on the components most likely to fail. |
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8. Chemicals and Petrochemicals |
8.1 Pumps |
Pumps are the workhorses of chemical plants. Cavitation, bearing wear, and seal failure are common. The system distinguishes between these causes and explains the evidence. An explanation of cavitation prompts a check of suction conditions, while an explanation of bearing wear prompts a lubrication check. |
8.2 Compressors |
Compressors are complex and expensive. The system monitors vibration, temperature, and pressure to detect surge, misalignment, and bearing issues. Explainable diagnostics help operators avoid emergency shutdowns. |
8.3 Heat Exchangers |
Fouling reduces heat transfer and increases energy consumption. The system can explain when a performance decline is due to fouling rather than a sensor error. |
8.4 Reactors |
Reactors require careful control of temperature and pressure. The system can detect anomalies in agitators and cooling systems and explain the likely cause. |
8.5 Flare Systems |
Flare systems must operate reliably for safety. The system monitors blowers and pilots and explains any anomalies. |
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9. Food and Beverage |
9.1 Mixers and Blenders |
Food processing equipment must be cleaned frequently. Bearings and seals are subject to washdown damage. The system detects early signs of wear and explains whether the cause is water ingress or lubrication loss. |
9.2 Conveyors and Sorters |
High-speed sorters use belts, rollers, and motors. Explainable diagnostics help maintenance teams keep lines running during peak seasons. |
9.3 Fillers and Cappers |
Fillers and cappers have many small moving parts. The system detects anomalies and explains whether the cause is mechanical wear or a setup issue. |
9.4 Refrigeration Compressors |
Refrigeration is critical for food safety. The system monitors compressors and explains anomalies such as lubrication breakdown or valve wear. |
9.5 Packaging Robots |
Packaging robots must run at high speed. The system monitors joints and grippers and explains any decline in performance. |
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10. Energy and Utilities |
10.1 Wind Turbines |
Wind turbines are remote and expensive to service. The system monitors gearbox and generator bearings and explains whether the cause is lubrication breakdown, misalignment, or fatigue. This allows operators to plan maintenance during low-wind periods. |
10.2 Gas Turbines |
Gas turbines are used for power generation and compression. The system monitors vibration and temperature and explains anomalies such as rotor imbalance or bearing wear. |
10.3 Hydroelectric Generators |
Hydro generators operate for decades. The system detects changes in vibration and explains whether they are due to bearing wear, misalignment, or runner erosion. |
10.4 Transformers |
Transformers are critical assets. The system can analyze dissolved gas data and explain whether a fault is thermal or electrical. |
10.5 Boilers and Steam Systems |
Boilers require reliable fans, pumps, and valves. Explainable diagnostics help operators maintain efficiency and safety. |
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11. Pharmaceuticals and Biotechnology |
11.1 Clean Room Fans |
Clean rooms require precise air flow. The system monitors fans and explains anomalies that could affect air quality. |
11.2 Bioreactor Agitators |
Bioreactors must maintain gentle, consistent mixing. The system detects agitator anomalies and explains whether the cause is bearing wear or seal leakage. |
11.3 Tablet Presses |
Tablet presses operate at high speed. The system monitors compression forces and explains when variations are due to mechanical wear rather than formulation changes. |
11.4 Lyophilizers |
Lyophilizers have vacuum pumps and refrigeration systems. Explainable diagnostics help avoid batch losses. |
11.5 Packaging Lines |
Pharmaceutical packaging lines must meet strict quality standards. The system helps maintain equipment reliability and explains any anomalies. |
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12. Mining and Heavy Equipment |
12.1 Crushers |
Crushers operate under extreme load and abrasion. The system detects bearing and gearbox issues and explains whether the cause is lubrication breakdown or contamination. |
12.2 Ball Mills |
Ball mills are critical for ore grinding. The system monitors bearings and drive systems and explains anomalies. |
12.3 Haul Trucks |
Haul trucks operate in remote locations. The system monitors engines, transmissions, and wheel motors and explains anomalies to help plan maintenance. |
12.4 Conveyors |
Mining conveyors can be kilometers long. The system monitors idlers, pulleys, and drives and explains anomalies to prevent fires and stoppages. |
12.5 Draglines and Shovels |
Draglines and shovels are massive machines with many failure points. Explainable diagnostics help maintenance teams prioritize repairs. |
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13. Pulp and Paper |
13.1 Paper Machines |
Paper machines are complex and continuous. The system monitors rolls, bearings, and drives and explains anomalies such as lubrication breakdown or misalignment. |
13.2 Pulp Digesters |
Digesters operate under high pressure and temperature. The system monitors agitators and pumps and explains anomalies. |
13.3 Boilers |
Pulp mills generate their own power. The system monitors boilers and explains anomalies to maintain steam supply. |
13.4 Wood Chippers |
Chippers handle tough material. The system detects bearing and knife issues and explains the cause. |
13.5 Wastewater Treatment |
Wastewater systems use pumps, blowers, and mixers. Explainable diagnostics help maintain compliance and avoid environmental issues. |
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14. Electronics and Semiconductors |
14.1 Vacuum Pumps |
Semiconductor manufacturing relies on vacuum pumps. The system monitors pump vibration and explains anomalies such as bearing wear or buildup. |
14.2 Wafer Handling Robots |
Wafer handling robots must be extremely precise. The system detects anomalies and explains whether the cause is bearing wear or contamination. |
14.3 Chillers |
Chillers provide precise temperature control. The system monitors compressors and explains anomalies. |
14.4 Air Handling Units |
Clean rooms require precise air flow. The system monitors fans and explains anomalies. |
14.5 Chemical Delivery Systems |
Chemical delivery systems use pumps and valves. Explainable diagnostics help avoid contamination and downtime. |
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15. Cross-Industry Patterns |
15.1 Rotating Equipment Dominates |
Across all these industries, rotating equipment is the most common target. Bearings, gears, and motors are everywhere. Explainable diagnostics that focus on these components deliver value in every sector. |
15.2 Lubrication Is a Recurring Theme |
Lubrication breakdown appears in nearly every industry. It is a root cause that engineers understand well, and it is often easy to fix if detected early. |
15.3 The Value of Early Detection |
In every industry, early detection reduces cost. A bearing replaced during a planned outage costs far less than a bearing that fails and damages a shaft. |
15.4 The Role of Collaboration |
In every industry, collaboration between AI and engineers improves outcomes. The AI provides detection and explanation. The engineer provides context and judgment. |
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16. Trust, People, and Organization |
16.1 Building Trust Over Time |
Trust is not granted overnight. It is built through repeated interactions in which the AI provides useful, accurate explanations and the engineer sees positive results. Early wins matter. A single correct diagnosis that prevents a failure can change a team's attitude. |
16.2 Training and Change Management |
Operators and maintenance engineers need training on how to use explainable AI. They need to understand what the system can and cannot do. Change management is as important as the technology. |
16.3 The Role of Feedback |
Feedback is essential. When engineers confirm or reject a diagnosis, they are teaching the system. This improves accuracy and builds ownership. |
16.4 Avoiding Alert Fatigue |
Too many alerts can overwhelm a team. Explainable AI helps by prioritizing alerts based on severity and confidence. A clear explanation also helps engineers decide quickly. |
16.5 Governance and Accountability |
Organizations need clear policies on how AI diagnoses are used. Who is responsible when an AI recommendation is followedWho reviews rejected diagnosesGood governance builds confidence. |
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17. Limitations and Open Challenges |
17.1 Data Quality |
Explainable AI depends on good data. Missing, noisy, or mislabeled data undermines explanations. |
17.2 Novel Failure Modes |
AI models are trained on known failure modes. When a new failure mode appears, the system may not recognize it. Human expertise remains essential. |
17.3 Integration Complexity |
Integrating AI with maintenance management systems, historians, and control systems is complex. Standards are still maturing. |
17.4 Cost and Scale |
Deploying AI across many sites is expensive. Cloud and edge architectures can help, but connectivity and security must be addressed. |
17.5 Evaluating Explanations |
How do we know an explanation is goodThis is an open research question. In industry, the best test is whether engineers find it useful and act on it. |
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18. Detailed Summary and Future Trajectories |
This chapter has examined explainable AI in industrial settings, with a focus on the black box problem and the Via Co-Pilot approach. The central argument is that industrial AI adoption depends not only on predictive accuracy but on the ability of systems to explain their conclusions in terms that engineers can validate and act upon. |
We began by defining the black box problem. Opaque models produce predictions without reasoning. In industrial operations, this opacity blocks adoption because engineers are accountable for decisions, need to validate recommendations against their experience, and require diagnoses that point to specific repairs. Regulatory and safety requirements add further pressure. |
We then introduced explainable AI. Transparency and interpretability are related but distinct. In industry, interpretability matters most. Post-hoc explanations can be useful but are approximations. Interpretable-by-design models are stronger because their inputs correspond to physical phenomena. Explanation must also be embedded in workflows, not delivered as a standalone report. |
We described the Via Co-Pilot approach. Its design principles are that every alert must have a cause, causes must be expressed in familiar terms, engineers must be able to respond, and explanations must lead to action. The system focuses on root causes such as bearing wear, lubrication breakdown, misalignment, imbalance, looseness, cavitation, corrosion, fouling, and electrical faults. Its interface presents a plain-language diagnosis, supporting evidence, and a recommended action. Collaborative workflows allow engineers to confirm, reject, or refine diagnoses, and confirmed diagnoses can trigger automated work orders. |
We then surveyed applications across many industries. In automotive manufacturing, the system monitors robotic joints, conveyors, stamping presses, paint shop fans, and welding robots. In aerospace and defense, it supports engine test cells, landing gear actuators, composite layup equipment, ground support equipment, and supply chain quality. In steel and metals, it monitors rolling mill bearings, furnace fans, continuous casters, hydraulic systems, and overhead cranes. In chemicals and petrochemicals, it covers pumps, compressors, heat exchangers, reactors, and flare systems. In food and beverage, it supports mixers, conveyors, fillers, refrigeration compressors, and packaging robots. In energy and utilities, it monitors wind turbines, gas turbines, hydro generators, transformers, and boilers. In pharmaceuticals, it supports clean room fans, bioreactor agitators, tablet presses, lyophilizers, and packaging lines. In mining, it covers crushers, ball mills, haul trucks, conveyors, and draglines. In pulp and paper, it monitors paper machines, digesters, boilers, chippers, and wastewater treatment. In electronics and semiconductors, it supports vacuum pumps, wafer handling robots, chillers, air handling units, and chemical delivery systems. |
Several cross-industry patterns emerged. Rotating equipment dominates. Lubrication breakdown is a recurring theme. Early detection delivers value everywhere. Collaboration between AI and engineers improves outcomes. |
We discussed trust, people, and organization. Trust is built over time through positive experiences. Training and change management are essential. Feedback improves the system and builds ownership. Alert fatigue must be avoided. Governance and accountability matter. |
We considered limitations. Data quality, novel failure modes, integration complexity, cost, and the challenge of evaluating explanations are all open issues. |
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Looking ahead, several trajectories are visible. First, explainable AI will become more integrated with control systems, allowing closed-loop optimization. Second, explanations will become more multimodal, combining text, visuals, and sensor data. Third, collaboration will deepen, with AI acting as a partner rather than a tool. Fourth, standards for explanation quality will emerge, driven by regulation and industry consortia. Fifth, edge computing will bring explainable diagnostics to remote and disconnected sites. Sixth, foundation models for industrial time series may provide broader coverage while retaining interpretability through physical constraints. |
The broader lesson is that explanation is not a feature added at the end. It is a design principle that determines whether AI is adopted or ignored. In industrial settings, where decisions have real consequences, the ability to explain is as important as the ability to predict. Via Co-Pilot illustrates how this principle can be put into practice, and the many industry examples show that the value is not limited to a single sector. As AI continues to spread across manufacturing and industrial operations, explainability will be the bridge between what machines can discover and what people are willing to do. |
cs@easiersoft.com